Macro-Ecological Patterns in Seed Removal by Animals

MACRO-ECOLOGICAL PATTERNS IN SEED REMOVAL BY ANIMALS

Si-Chong Chen

Thesis submitted for the degree of Doctor of Philosophy

Evolution and Ecology Research Centre

School of Biological, Earth and Environmental Sciences

UNSW, Australia

June 2016 PLEASE TYPE THE UNIVERSITY OF NEW SOUTH WALES Thesis/Dissertation Sheet

Surname or Family name: Chen

First name: Sichong Other name/s:

Abbreviation for degree as given in the University calendar: PhD

School: School of Biological, Earth and Environmental Sciences Faculty: Faculty of Science

Title: Macro-ecological patterns in seed removal by animals

Abstract 350 words maximum: (PLEASE TYPE)

This thesis aims to improve our understanding of several long-held ideas concerning seed removal by animals across large-scale gradients. By assembling a database of 13,135 animal-seed interactions across all vertebrate taxa, I provided the first broad test of the idea that large animals ingest large seeds. Surprisingly, I found that the size of ingested seeds was significantly negatively correlated with animal body weight. This negative relationship was driven by large animals, particularly ungulates, ingesting small seeds. The resu lts show that the loss of large animals could have negative effects on the dispersal of small-seeded plants, in addition to the more widely acknowledged impacts on large seeded plants. Next, I used data for 4008 Australian species to provide the first quantitative analysis of the idea that fleshy fruits are more prevalent towards the tropics. Plants were more likely to bear fleshy fruits at low latitudes, and in regions with warm, wet and stable climates. Fruit type was more strongly affected by conditions during the parts of th e year in which they grow than by conditions during the harshest parts of the year, suggesting that some current theories on plant traits may focus on the wrong aspects of climate. Finally, I performed a field study across 25 sites spanning 28° of latitude along the east coast of Australia, to provide the first empirical test of the idea that seed predation and seed defense are greater towards the tropics. Contrary to traditional expectations, neither seed predation nor seed physical defence was more intense at low latitudes. In fact, pre-dispersal predation and defence were greater at higher latitudes. My results are consistent with recent findings on latitudinal gradients in herbivory and defences in leaves. My findings cast further doubt on the generality of latitudinal gradients in biotic interactions, and suggest that increased seed/seedling mortality as predicted by the Janzen-Connell hypothesis does not provide a plausible explanation for the greater diversity of tropical ecosystems. My thesis has tested several well-accepted ideas on seed removal by animals, and shown that our understanding of the factors that shape global patterns in biodiversity needs to be reshaped.

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‘I hereby grant the University of New South Wales or its agents the right to archive and to make available my thesis or dissertation in whole or part in the University libraries in all forms of media, now or here after known, subject to the provisions of the Copyright Act 1968. I retain all proprietary rights, such as patent rights. I also retain the right to use in future works (such as articles or books) all or part of this thesis or dissertation. I also authorise University Microfilms to use the 350 word abstract of my thesis in Dissertation Abstract International (this is applicable to doctoral theses only). I have either used no substantial portions of copyright material in my thesis or I have obtained permission to use copyright material; where permission has not been granted I have applied/will apply for a partial restriction of the digital copy of my thesis or dissertation.'

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Date ……………………………………………...... Acknowledgements

How time flies and the moment when I landed in Australia seems like yesterday. I had never

thought I might do a PhD in Australia, but now I have been living in Sydney for three and a half years, met many helpful people and had a lot of interesting stories.

I constantly feel so lucky to have Angela Moles as my supervisor. She has been teaching me

how to do good science and become a good scientist with great patience. She always

encourages me to develop my own research ideas while also gives me a helpful hand when

necessary. Her timely supervision is my lifeguard in postgraduate surf to overcome

difficulties, keep enthusiasm, and (most importantly) learn swim myself. Besides the above

aspects, Angela herself is the idol that encourages me to seek a research career. It is difficult

to express my gratitude to her in just a few lines, so allow me to modify Titanic movie quotes

(may seemingly funny, but I mean it): she supervised me in every way that a student can be

supervised. Thank you, Angela!

Many thanks to my co-supervisor Stephen Bonser, my panel chairperson Rob Brooks, and panel members David Eldridge and Mike Letnic, for giving me sufficient support and

keeping my study on track.

To my academic siblings Floret Meredith (my lab bestie is awesome!), Habacuc Flores-

Moreno, Rhiannon Dalrymple, Claire Brandenburger, Stephanie Creer, Tim Hitchcock, Tom

Meredith, Martin Kim, Marianne Tindall, to our lab postdocs Riin Tamme and Julia Cooke,

and to the visiting scholars Hong-Xiang Zhang and Yan-Hong Wang. You have made the Big

Ecology Lab a real home, and you never let me sink in depression. Thank you for helping me in all aspects and witnessing every step of my progress.

Thank you to Will Cornwell and Frank Hemmings for the awesome collaboration in my

projects and the introduction to statistics and plants. Thank you to Casey Gibson, Charlotte

Mills, Jie Yan, Linda Wong and Uncle Deng for being brilliant companions in my fieldtrip. I

am sure the beautiful scenery and the hardworking sweat will all be treasured up in our hearts.

Thank you to Yun-Hong Tan, Jia-Jia Liu, De-Li Zhai, Zhi-Cong Dai, Shan-Shan Qi and Yan-

Jie Liu for constant encouragement and beneficial discussion.

I thank E&ERC and School of BEES for providing me awards, prizes and numerous opportunities to make a fantastic postgraduate experience, as well as Ecological Society of

Australia for the Student Research Grant.

I also sincerely thank my thesis examiners, Anna Traveset and Marc Johnson, for the valuable comments on helping me to make this thesis a better one.

Lastly, I want to sincerely thank my mum. She is the first teacher introducing me to the nature, and her persistence to seek her PhD (in molecular immunology) has greatly inspired me to seek my own. I moulted several times during these years (both literally and figuratively), but it is my mum’s love that helped me to conquer each “itch” and come back to a healthy life. She read my papers, assisted me in the fieldwork and constantly talked with me about my projects. Thank you, mum, for everything.

Abstract

This thesis aims to improve our understanding of several long-held ideas concerning seed removal by animals across large-scale gradients.

By assembling a database of 13,135 animal-seed interactions across all vertebrate taxa, I provided the first broad test of the idea that large animals ingest large seeds. Surprisingly, I found that the size of ingested seeds was significantly negatively correlated with animal body weight. This negative relationship was driven by large animals, particularly ungulates, ingesting small seeds. The results show that the loss of large animals could have negative effects on the dispersal of small-seeded plants, in addition to the more widely acknowledged impacts on large-seeded plants.

Next, I used data for 4008 Australian species to provide the first quantitative analysis of the idea that fleshy fruits are more prevalent towards the tropics. Plants were more likely to bear fleshy fruits at low latitudes, and in regions with warm, wet and stable climates. Fruit type was more strongly affected by conditions during the parts of the year in which they grow than by conditions during the harshest parts of the year, suggesting that some current theories on plant traits may focus on the wrong aspects of climate.

Finally, I performed a field study across 25 sites spanning 28° of latitude along the east coast of Australia, to provide the first empirical test of the idea that seed predation and seed defense are greater towards the tropics. Contrary to traditional expectations, neither seed

iii

predation nor seed physical defence was more intense at low latitudes. In fact, pre-dispersal predation and defence were greater at higher latitudes. My results are consistent with recent

findings on latitudinal gradients in herbivory and defences in leaves. My findings cast further

doubt on the generality of latitudinal gradients in biotic interactions, and suggest that

increased seed/seedling mortality as predicted by the Janzen-Connell hypothesis does not

provide a plausible explanation for the greater diversity of tropical ecosystems.

My thesis has tested several well-accepted ideas on seed removal by animals, and shown that

our understanding of the factors that shape global patterns in biodiversity needs to be

reshaped.

Drawing by Si-Chong Chen.

Statement of contribution of co-authors and declarations of permission to publish

Chapters one to four comprise for stand-alone paper that have been prepared for publication in peer-reviewed scientific journals. Each chapter is self-contained including introduction, methods, results, discussion, acknowledgments, references, tables, figures and appendices.

The formatting of each chapter is in the style of the journal to which it has been/will be submitted. The contributions by co-authors for each chapter are listed below.

Chapter one

Chen, S.-C. & Moles, A.T. (2015) A mammoth mouthful? A test of the idea that larger animals ingest larger seeds. Global Ecology and Biogeography, 24, 1269-1280.

SCC and ATM designed the study. SCC collected and analysed the data. The manuscript writing was led by SCC, with contribution from ATM.

This article has been reproduced in this thesis with the permission of the Global Ecology and

Biogeography and the Wiley-Blackwell Publishing.

Chapter two

Chen, S.-C., Cornwell, W.K., Zhang, H.-X. & Moles, A.T. (in press) Plants show more flesh in the tropics: variation in fruit type along latitudinal and climatic gradients. Ecography (DOI:

10.1111/ecog.02010)

SCC designed the study. SCC and HXZ collected the data. SCC and WKC performed statistical analyses, with contribution from ATM. The manuscript writing was led by SCC, but ATM substantially contributed to it. All of the authors contributed to the project’s final conception.

This article has been reproduced in this thesis with the permission of the Ecography and the

Nordic Society Oikos Publishing.

Chapter three

Chen, S.-C., Hemmings, F.A., Chen, F. & Moles, A.T. Is there a latitudinal gradient in seed predation?

SCC and ATM designed the study. SCC, FAH and FC collected the data. SCC performed statistical analyses and wrote the first draft of the manuscript, and ATM contributed substantially to the later versions. All of the authors contributed to the project’s final conception.

Chapter four

Chen, S.-C. & Moles, A.T. Could a latitudinal gradient in seed defence be obscuring the latitudinal gradient in seed predation?

SCC and ATM designed the study. SCC collected and analysed the data. The manuscript writing was led by SCC, with guidance from ATM.

Table of contents

Acknowledgements ...... i

Abstract ...... iii Statement of contribution of co-authors and declarations of permission to publish ...... v

Introduction ...... 1

Chapter one A mammoth mouthful? A test of the idea that larger animals ingest larger seeds ...... 23

Chapter two Plants show more flesh in the tropics: variation in fruit type along latitudinal and climatic gradients ...... 113

Chapter three Is there a latitudinal gradient in seed predation? ...... 145

Chapter four Could a latitudinal gradient in seed defence be obscuring the latitudinal gradient in seed predation? ...... 187

Conclusions ...... 221

Introduction

My original ideas on this thesis were inspired by a proverb said by a Chinese philosopher

Gào Zĭ 2,300 years ago, which I translate as “Feeding and reproducing are nature of all

organisms.” Fruits and seeds are important food sources for animals (Vander Wall & Beck,

2012). It is estimated that over 5,000 vertebrate species regularly eat fruits/seeds and disperse seeds (Vander Wall & Beck, 2012). Fruit-eating is particularly common in mammals and birds (Jordano, 2000; Herrera, 2002), while a variety of reptiles (Whitaker, 1987; Pérez-

Mellado & Traveset, 1999; Olesen & Valido, 2003) and fish (Horn et al., 2011) are also active frugivores. The only know frugivorous amphibian is a tree frog species Xenohyla truncata (da Silva et al., 1989; da Silva & de Britto-Pereira, 2006). Records of seed ingestion also exist in invertebrates such as slugs (Gervais et al., 1998; Calvino-Cancela & Rubido-

Bará, 2012), beetles (de Vega et al., 2011), and weta (Duthie et al., 2006; Fadzly & Burns,

2010). In turn, seed removal by animals has important impacts on plant demography (Howe

& Smallwood, 1982; Wang & Smith, 2002; Vander Wall et al., 2005), leaving the seed with one of two outcomes – seed dispersal, or seed predation (Forget et al., 2005).

SEED DISPERSAL

The first two chapters of this thesis focus on internal seed dispersal by animals. Seed dispersal, the movement of seeds away from the parent plant, allows plants to reach and potentially colonize new habitats (Willson, 1993; Levin et al., 2003), and facilitates the regeneration of plant populations and communities (Wunderle, 1997; Willson & Traveset,

2000). Animal dispersers often bring seeds additional advantages to the general benefits of seed dispersal (Herrera, 2002). For example, seed germination percentage and germination speed are significantly increased after passage through animal digestive tracts (Traveset &

Introduction

Verdú, 2002). In addition, vertebrates disperse seeds significantly further than is achieved

with other dispersal vectors (Thomson et al., 2011; Tamme et al., 2014). Increased dispersal

distance promotes gene flow between populations (Bohonak, 1999), and offers seeds greater

chance to escape from density- or distance-dependent seed and seedling mortality near

mother plants (Janzen, 1970; Connell, 1971).

Previous studies have identified a wide range of animal-seed interactions for specific plant

species, (e.g. Traveset, 1995; Campos et al., 2012), for specific animal species (e.g. Traveset,

1990; O’Farrill et al., 2013), and for specific communities (e.g. Bollen et al., 2004; Donatti et

al., 2011; Nogales et al., 2015). However, we still have much to learn about the animal-seed

interactions, as many foundational ideas in the field are yet to be put to a broad empirical test,

such as the roles of animal size and/or seed size (Woodward et al., 2005).

Scaling relationship between animal size and seed size

I start this thesis by quantifying the relationship between animal size and seed size. Scaling

relationships are prevalent in nature (Damuth, 2001), and much previous work on fruit-

frugivore interactions has been based on the assumption that larger animals ingest larger

seeds ("fruit-size hypothesis"; Mack, 1993; Jordano, 1995; Izhaki, 2002; Lord, 2004). On the

animal side, body size plays an influential role in the structure and dynamics of many

ecological networks (Woodward et al., 2005). For example, birds that have specialized diets of fruits usually have larger body mass and gape size than their non-specialized counterparts, providing them with less limitation to feed on larger fruits and wider varieties of fruits

(Herrera, 2002). On the plant side, fruit/seed sizes exhibit strong co-evolutionary patterns

Introduction

with frugivore characteristics (Wheelwright, 1985; Bolmgren & Eriksson, 2005). Large seeds are often preferred for higher nutrient content ("optimal diet theory", Sih & Christensen,

2001), but the preferences for larger seeds may also cause increased costs in handling, ingesting or carrying seeds.

A few studies on avian frugivores have found evidence of a positive relationship between animal body mass and ingested seed size (Wheelwright, 1985; Cath & Catterall, 2010; Burns,

2013). However, we do not know whether this positive relationship is a universal rule across the full suite of animal-seed interactions including different frugivore taxa and different fruit types. I address this knowledge gap in Chapter 1.

The explosion of publications in ecology and evolution means that it is now possible to synthesize data from papers and databases to quantify general trends and patterns in one’s study field. Data synthesis allows researchers to quantify patterns at a larger scale than would be possible in an empirical study. Thousands of studies on the feeding ecology for single animal species or a small group of related animal species have been reported through the last few decades. These empirical observations are of great value to get a better picture of trait coupling in animal-seed interactions.

In Chapter 1, I provide the most comprehensive quantification of the relationship between animal body mass and ingested seed size, using a global compilation of 13,135 unique animal-seed interactions across all vertebrate groups (seven fish species, one amphibian species, 42 reptile species, 313 bird species and 224 mammal species). I hope that my data synthesis will inform our understanding on which plant species might suffer dispersal difficulties in the era of defaunation (Dirzo et al., 2014).

Introduction

Latitudinal and climatic gradients in fruit type

My second chapter focuses on biogeographic patterns in fruit type. Fruit type has a major

impact on seed dispersal. For example in Chapter 1, I showed that the scaling relationships

between animal body mass and ingested seed size are significantly different between fleshy

fruits and dry fruits.

Fleshy fruits can be found in various habitats from boreal regions to the tropics (Tiffney,

1984; Lorts et al., 2008). Variation in the frequency of fleshy fruits has been surveyed within

various communities, from tropical rainforests to alpine tundras (Willson et al., 1989;

Jordano, 2000). Taking Australian vegetation as an example, fleshy-fruited plants account for

over 80% of woody species in the wet tropics, 35-70% in the dry tropical forests, 10-50% in

the scrublands, and less than 10% in heaths and alpine communities (see synthesis in Willson

et al., 1989; Jordano, 2000). The distribution of fleshy-fruited plants had only been qualitatively associated with abiotic gradients (e.g. precipitation, Gentry, 1982), until two recent analyses that quantified relationships between the frequency of dispersal modes (which were assigned based on fruit type and morphology) and bioclimatic variables in Neotropical forests (Almeida-Neto et al., 2008; Correa et al., 2015). It remains unclear whether the

frequency of fleshy versus dry fruit varies along latitudinal and climatic gradients, at a broad

geographic scale.

Determining geographic variation in plant traits can improve our understanding of the

potential impacts of changing climates on plants’ life history strategies and processes (De

Frenne et al., 2013). The digitalization of numerous floras and online databases make data

Introduction

more accessible and facilitate broad-scale studies of plant traits. In Chapter 2, I compile fruit

types for 4008 Australian species, spanning 833,757 geographic occurrences. I used these

data to provide the first quantification of how the proportion of plant species bearing fleshy

fruits correlates with latitude and 15 climatic variables. I identify which climatic variable and

which group of climatic variables (mean, extremes, or variations) are most closely correlated

with the proportion of fleshy-fruited species. I hope my analyses will advance our

understanding on the biogeography of plant reproduction strategies.

SEED PREDATION

The last two chapters of this thesis focus on latitudinal gradients in seed predation and seed

defence against seed predators. Seed predation, also known as granivory, can happen any

time in the period from seed formation to seedling establishment, including before seed

dispersal (when seeds are still on the mother plant), and after seed dispersal (when seeds have moved away from the mother plant; Crawley, 1992). Individual plants (seeds) are killed

during seed predation (Kolb et al., 2007), consequently affecting plant population dynamics,

demography (Hulme, 1998; Hulme & Benkman, 2002), the maintenance of life-history variation and evolutionary dynamics (Agrawal et al., 2013). A generation of seeds can range from completely escaping predators (examples in Kolb et al., 2007) to being completely eradicated (e.g. Jensen, 1982). Crawley (1992) estimated that, on average, only a quarter of

seeds survive predation to germinate.

Introduction

The latitudinal gradient in seed predation

The diversity and abundance of seed predators vary across different ecosystems (Hulme &

Benkman, 2002), and the rates of seed predation dramatically vary in space and time (see

Table 1 & 2 in Hulme & Benkman, 2002; Kolb et al., 2007). The interactions between seeds and seed predators are thought to be stronger in the tropics (Janzen, 1970; Connell, 1971).

More intense and more specialised biotic interactions in the tropics have been suggested as a potential cause of the high biodiversity of tropical ecosystems (Schemske et al., 2009; Coley

& Kursar, 2014). However, this theory has been recently challenged by several recent data syntheses and meta-analyses (summarized in Moles & Ollerton, 2016). The debate is particularly heated on the latitudinal gradient in herbivory (Lim et al., 2015; Moles &

Ollerton, 2016). Two intraspecific studies suggest that latitudinal variation in herbivory could be different across plant tissue types (Anstett et al., 2014) or across herbivore guilds (Moreira et al., 2015). Seed herbivory differs from leaf herbivory in several respects (Zangerl &

Bazzaz, 1992; Hanley et al., 2007), but its geographical pattern has been much less studied.

Introduction

(a)

(b)

Figure 1. Sclerophyll forests in (a) Bundjalung National Park (29.3395°S, 153.2610°E), New

South Wales, Australia, and (b) Eurobodalla National Park (36.0951°S, 150.1197°E), New

South Wales, Australia. Photos by Si-Chong Chen.

Introduction

There have been a few intraspecific studies of the latitudinal gradient in seed predation

(García et al., 2000; Toju & Sota, 2006; Alexander et al., 2007; Montesinos et al., 2010;

Verhoeven & Biere, 2013; Anstett et al., 2014; Kambo & Kotanen, 2014; Sanz & Pulido,

2014; Lee & Kotanen, 2015; Moreira et al., 2015). These studies have yielded mixed results of the latitudinal gradient in seed predation and they are all from the northern hemisphere. A global cross-species compilation revealed no latitudinal gradient in overall rates of seed predation (Moles & Westoby, 2003). Data syntheses that are based on multiple single-species studies or studies within particular taxa or regions allow unprecedented quantifications of large-scale patterns, but do have limitations. For example, the interpretation of outcomes could be complicated by the diverse methods that are applied in the synthesized studies

(discussed in Orrock et al., 2015). I therefore complement my data syntheses (Chapters 1 and

2) with large-scale field studies with a consistent protocol. In Chapter 3, I measure seed predation in sclerophyll vegetation (Figure 1) across 25 sites from 15°30'S to 43°35'S on the east coast of Australia (Figure 2), to provide the first quantification of the latitudinal gradient

in seed predation at both interspecific and intraspecific levels.

Latitudinal gradient in seed physical defence

In contrast with other plant tissues (e.g. leaves, succulent pulp or lignified fruit husks), seeds

contain highly concentrated energy (Hulme & Benkman, 2002; Vander Wall & Beck, 2012).

Since seeds are highly sought after, they develop an assemblage of traits to increase the costs

of seed handling and to reduce predation (“Handling Costs Hypothesis”, Vander Wall &

Beck, 2012). For example, some seeds are extremely toxic, producing chemical compounds

that can be lethal at very low concentrations (Hulme & Benkman, 2002). Mechanical

Introduction defensive traits are obvious (Figure 3), as there are rarely any modern naked seeds (Mack,

2000).

Figure 2. Annual mean temperature map of Australia, showing the location of the sites used for field study (black dots). Map was created using mean annual temperature data (30 seconds resolution, 1950-2000) from the WorldClim v1.4 dataset

(http://www.worldclim.org/).

Introduction

(a)

(b)

Figure 3. (a) Seeds of Hakea species are found in large woody follicles. The thick husk may help to deter seed predators. However, entry/exit holes of invertebrates are often found in the follicles, leaving only frass or seed fragments inside. (b) Seeds of Xanthorrhoea macronema that have been severely damaged by invertebrates. Photos by Si-Chong Chen.

Introduction

My large-scale empirical study in Chapter 3 revealed that seed predation were not more

intense towards lower latitudes, raising the possibility that seeds might be better defended

towards the tropics. However, geographic patterns in seed resistance to herbivores (such as

physical or chemical defences) have received surprisingly little attention, compared with the

plenty of leaf resistance studies (see the literature synthesized in Moles et al., 2011). In

addition, the association between herbivory and herbivory resistance has been contentious

(Carmona et al., 2011). One theory is that higher levels of herbivory will result in directional selection toward higher levels of defense, leading to a coevolutionary arms race between plants and herbivores (Coley & Kursar, 2014), whereas an intraspecific study suggests that seed predation is lower where protective pericarp is thicker (Toju et al., 2011). The scarcity

of data leaves us unclear as to whether seeds are better defended in high predation

communities, or whether sites with high rates of seed loses have led plants to develop heavily defended seeds. In Chapter 4, I provide the first cross-species test of the hypothesis that seeds are better physically defended towards low latitudes, by measuring mass ratios of protective tissue to seed reserve for 250 species × site combinations, as well as quantifying the relationship between seed defense and seed predation.

In summary, this thesis focuses on large-scale patterns in seed removal and seed functional traits, providing the first quantitative tests of several long-held ideas that underpin ecological theories. I hope that my findings will enhance our understanding in the field of animal-seed interactions at the continental and global levels.

Introduction

Each of the chapters of this thesis has been designed and written as a stand-alone paper for publication in ecological journals, therefore some degree of overlap between chapters has been unavoidable. The first person plural “we” and the possessive adjective “our” are used throughout chapters 1 to 4 as none of them are sole-authored. However, I am the person responsible for the majority of the concepts, data collections, data analyses and writing in each chapter. Co-authors and their contributions are listed in each chapter in the preface.

Introduction

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Introduction

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Introduction

De Frenne, P., Graae, B.J., Rodríguez‐Sánchez, F., Kolb, A., Chabrerie, O., Decocq, G., Kort,

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Chapter one

A mammoth mouthful?

A test of the idea that larger animals ingest larger seeds

Global Ecology and Biogeography, 24, 1269-1280

Si-Chong Chen1 & Angela T. Moles1

1 – Evolution & Ecology Research Centre, School of Biological, Earth and Environmental

Sciences, The University of New South Wales, Sydney, NSW 2052, Australia

Seed size and animal size

ABSTRACT

Aim It has been widely assumed that large seeds generally require large animals to ingest and

disperse them. However, this relationship has only been quantified in single animal groups

(e.g. birds) and in a few communities. Our goal was to provide the first broad-scale study of

the relationship between animal body mass and ingested seed size.

Location Global.

Methods We compiled a dataset of 13135 unique animal × seed interactions, animal body

masses and seed sizes in these interactions, across all vertebrate groups (fish, amphibians,

reptiles, birds and mammals).

Results Contrary to expectations, ingested seed size was negatively related to animal body

mass. This negative relationship was largely driven by large ungulates ingesting small and

dry seeds, and analyses excluding either ungulates or seeds with non-fleshy fruit types showed a positive relationship between animal body mass and ingested seed size. Large animals ingested both larger maximum sizes of seeds (the 95th quantile had a positive slope)

and smaller minimum sizes of seeds (the 5th quantile had a negative slope). Larger animals

ingest larger seeds from fleshy fruits but smaller seeds from non-fleshy fruits. A significant positive relationship was found between animal size and the number of ingested seed species.

Main conclusions Our data show that one of the assumptions that has underpinned the study

of animal-seed interactions does not hold true across the full range of animal taxa and fruit types. These findings shed new light on theories about which types of plant species might be at risk if large animals go extinct, and cast doubt on the generality of a few theories (e.g.

Seed size and animal size

optimal diet theory, fruit-size hypothesis) in shaping the relationship between frugivores and seeds.

Keywords

Body mass; feeding ecology; frugivory; animal-plant interaction; seed dispersal; seed

ingestion; seed size

Seed size and animal size

INTRODUCTION

It has been widely assumed that large diaspores generally require large animals to ingest and disperse them (Mack, 1993; Jordano, 1995; Lord, 2004). The biased loss of large-bodied frugivores (e.g. by hunting, habitat loss, or climate change; Dirzo et al., 2014) is predicted to affect dispersal of large-seeded species over small-seeded species (Cramer et al., 2007).

Despite the widespread awareness of the importance of body size in a variety of networks

(Woodward et al., 2005), the relationship between seed size and animal body size has not been quantified across the full suite of potential seed dispersers. Most previous studies of the relationship between body mass and ingested seed size have focused on avian frugivores

(Wheelwright, 1985; Cath & Catterall, 2010; Burns, 2013). In this paper, we provide the most comprehensive quantification of the relationships between animal body mass and ingested seed size across all vertebrate groups, including fish, amphibians, reptiles, birds and mammals.

We began by testing the hypothesis that there is a positive relationship between animal body mass and the average size of ingested seeds. According to optimal diet theory, fruit-eating animals should prefer larger diaspores that yield more energy per unit foraging time, and may drop smaller diaspores from their diet to maximize foraging efficiency (Sih & Christensen,

2001). This idea has consistently been supported by evidence at various scales. For example, there is a positive relationship between gape widths of avian frugivores and average sizes of ingested fruits (Wheelwright, 1985; Cath & Catterall, 2010; Burns, 2013). The average size of ingested fruits is positively correlated with the forearm length of neotropical fruit bats

(Heithaus et al., 1975; Kalko et al., 1996). Similarly, the “fruit-size hypothesis” suggests that animal size and diaspore size act together to determine an animal’s ability to ingest fruits of a given size (Izhaki, 2002). For example, primates tend to swallow small seeds but prefer to

Seed size and animal size spit out large seeds (Lambert, 1999; Stevenson et al., 2005). Local-scale studies also suggest that diaspore dimensions could be the most predictable morphological trait determining frugivores’ selections, as animals choose fruit/seed size positively in accordance with their body masses (Gautier-Hion et al., 1985; Debussche & Isenmann, 1989; Bollen et al., 2004;

Donatti et al., 2011). We aim to determine whether these positive patterns hold at a broader scale and across diverse taxa.

Although we predict a positive relationship between animal size and seed size, we do not expect this to be a simple linear relationship. We hypothesize that there is a positive triangular relationship between seed size and animal size, with large animals ingesting both large and small seeds, while small animals are only able to ingest small seeds. This prediction is based on the idea that there will be a physical limit to the maximum size of seeds that a given animal can ingest (Mack, 1993; Burns, 2013). For example, gape sizes of birds limit the maximum diameters of the swallowed diaspores, but not the minimum diameters

(Wheelwright, 1985; Bollen et al., 2004). Debussche & Isenmann (1989) studied the diets of eight frugivorous birds and two omnivorous mammals, and found that maximum fruit volumes were positively correlated with the animals’ body masses while minimum fruit volumes were not. However, shape of the relationship between body mass and diaspore size has not been quantified across a large range of natural interactions at a broad scale.

The fruits that animals ingest span a wide range of forms, from succulent drupes to dry nuts

(Bruun et al., 2008; Koike, 2009). Previous studies and observations suggest that fleshy fruits are often positively sought out and ingested, while non-fleshy fruits may be grazed unintentionally during grazing or browsing (Janzen, 1984; Gautier-Hion, 1985). We extend these observations to the prediction that animal body mass will be positively correlated with

Seed size and animal size

the seed size of ingested fleshy fruits, but not significantly correlated with the seed size of

non-fleshy fruits.

In addition to an overall size scaling relationship, we also expect that the slopes and shapes of

the relationships between body mass and seed size will vary across animal taxa. The digestive

characteristics and foraging behaviours vary across animal taxa, making within-taxon

analyses important and interesting. We begin by exploring the relationships between body

mass and seed size in each major animal taxonomic group. Then, we test two hypotheses

based on animal form and function. First, we compare the relationships between body mass

and seed size across animals with and without grinding teeth. Dentition influences the

animals’ treatments of the seeds during ingestion. Many animals have well-developed grinding/crushing teeth, so that digestion begins in the oral cavity. They are capable of masticating relatively larger seeds by molar mill, making the size of ingested seed less restricted by the physical limits of digestive tract. In comparison, taxa that tend to swallow seeds without fine chewing are less likely to ingest seeds exceeding their gapes (Dearing &

Schall, 1992; Varela & Bucher, 2002). Therefore, in the taxa without fine chewing, we predict that the sizes of ingested seeds will be more tightly and positively restricted by the size of digestive tracts. Second, we compare the relationship between body mass and seed size across flying animals and non-flying taxa. Previous empirical studies and the assumption of a positive relationship between animal size and diaspore size were mostly based on flying taxa (Eklöf et al., 2013). However, animals with different movement modes may be differentially affected by ingested seeds (Vidal et al., 2013). Flying taxa are expected to be more affected by heavy ballast than are taxa that move by running, walking or crawling

(Schmidt-Nielsen, 1972; Vidal et al., 2013). Therefore, we test the hypothesis that flying

Seed size and animal size

animals will show a tighter and more positive relationship between body mass and seed size

than will non-flying taxa.

Finally, we test the idea that large animals ingest a higher diversity of seed species.

According to the passive foraging model (Burns, 2013), body masses will be positively

associated with the maximum sizes of ingested seeds. If this is the case, then larger animals

will have potential to ingest a wider diversity of seeds. Further, the larger home ranges of

larger animals (Jetz et al., 2004) might enable them to encounter more species of fruiting plants, and thus have more food choice. By taking diverse seeds, large animals may achieve a more balanced diet and absorb a lower proportion of toxins (Freeland, 1991), consistent with their high nutritive demand and low metabolic detoxification (Campos-Arceiz et al., 2008).

Some empirical studies have supported a positive relationship between body features (e.g. body mass, gape size) and seed dietary breadth. For example, birds with larger gape width ingest more fruit species (17 species of birds in Wheelwright, 1985), and larger even-toed

ungulates ingest a greater number of seed species (seven species of ungulates in Gautier-Hion et al., 1985). Besides these occasional syntheses of feeding ecology for a small group of related species and the numerous reports of feeding ecology for single animal species (e.g.

Campos-Arceiz et al., 2008; O’Farrill et al., 2013), ours will be the first quantification of the relationship between body mass and seed diversity ingested across a broad range of vertebrate taxa.

In summary, we aim to quantify the relationship between animal body mass and ingested seed size across all vertebrate groups. Specifically, we will:

(1) Test the hypothesis that the average size of ingested seeds is positively correlated with

animal body mass;

Seed size and animal size

(2) Test the hypothesis that there is a triangular relationship between body mass and the

ingested seed size, with a positive upper boundary (the 95th quantile) but non-

significant lower boundary (the 5th quantile);

(3) Test the hypothesis that animal body mass is positively correlated with the seed size of

ingested fleshy fruits, while not correlated with the seed size of ingested non-fleshy

fruits.

(4) Quantify relationships within each major taxonomic group, and test hypotheses about

the effect of oral mastication and movement mode on the relationships between

animal body mass and ingested seed size;

(5) Test the hypothesis that large animals ingest a greater number of seed species than do

small animals.

METHODS

Data compilation

Our study concerned vertebrates ingesting seeds, and the replicates were animal × plant interactions. Published data for vertebrate species and the seed species in their diet were located using the Institute for Scientific Information (ISI) Web of Knowledge (Accessed in

2013) with the search terms “(f$eces OR f$ecal OR dung$ OR scat$ OR dropping$ OR defecat* OR ingest* OR endozoochor* OR "gut content$" OR "stomach content$" OR

Seed size and animal size

"gizzard content$" OR diet OR "food selection" OR "food resource$" OR foraging OR

frugivor* OR “seed predat*”) AND (seed$ OR fruit$ OR dispers*)”. We also searched for

relevant papers in the reference lists of some focal papers. The plants in each animal’s diet

were recorded to species level where possible, but the unknown or undetermined plant

species that had seed dimension information in the original papers were also included.

Seed ingestion, rather than seed fate or seed dispersal quality, was the focus of this study.

Only primary ingestion in natural conditions was included. That is, we tried our best to

eliminate interactions in which the seeds did not go through the throat (e.g. ejecta pellets

from bats, spit-out seeds and cheek-pouch seeds from primates, cached seeds from rodents).

We also excluded all forms of artificial feeding trials to captive animals, livestock feeding on

cultivated plants, secondary ingestion (e.g. Padilla et al., 2012), and synzoochory (e.g.,

adhesion, scatter hoarding). Overall, 15245 animal × plant interactions were extracted from

339 published papers, including 237 mammals, 318 birds, 48 reptiles, 1 amphibian and 8 fish.

Data were from locations ranging from 74°30’ N to 50°00’ S, including a wide range of

ecosystem types from arctic tundra to tropical rainforest (Fig. 1). An overview of the data

collection and a list of literature where we extracted data from are presented in Appendix S1.

The animal × plant interactions mostly fall in to three categories: (1) intact seed identification

or germination from faeces (55%); (2) intact seed identification or germination from gut

content (5%); (3) observations of animals ingesting fruits or seeds, including seed

identification in seed traps and regurgitated pellets (29%); and a mix of these sources (11%).

The results of our main analyses were qualitatively similar when done including all data and

when performed using just those data collected through analysis of faeces (Appendix S2).

Faecal data were used for this comparison because seeds found in faeces were those that travelled through animal guts. That is, our main results are neither obscured, nor artificially

Seed size and animal size strengthened by the inclusion of different types of data. We have presented the results of analysis including all the data throughout the rest of the manuscript, as these analyses have the greatest statistical strength, taxonomic breadth and geographic scope.

Body mass data were extracted from the same papers as the data for animal × plant interactions whenever possible. Otherwise, geometric mean body mass data were taken from several pre-existing databases. A list of data sources is presented in Appendix S3. Overall, we were able to find body mass data for 13135 unique animal × plant interactions (224 mammals,

313 birds, 42 reptiles, 1 amphibian and 7 fish with body mass data). We used this dataset to quantify the relationship between body mass and number of seed species the animals ingest.

Figure 1 World map showing the distribution of sites included in this study. Each dot may encompass more than one location due to point size.

Seed size and animal size

Many previous studies measured fruit size rather than seed size (e.g. Debussche & Isenmann,

1989; Lord, 2004; Burns, 2013). Although the dimensions of fruit and seed are positively

related to each other (Moles et al., 2005; Donatti et al., 2011), we argue that seed size is more

relevant to ingestion than is fruit size for four reasons. 1) Fruit dimensions can be changed because of squeezing and can even exceed the size of digestive tract (Wheelwright, 1985;

Galetti et al., 2000; Reys et al., 2009; Burns, 2013). 2) An animal’s incapability to swallow

the whole fruit does not necessarily affect the ingestion of the seeds, as many fruits are eaten

piecemeal (e.g. species in the Moraceae and Solanaceae; Wheelwright, 1985; Cath &

Catterall, 2010; Burns, 2013). 3) The ingested seed is costly as a kind of “ballast” to

frugivores (Lambert, 1999) and is meaningful as the ultimate unit of plant reproduction. 4)

Ingested seeds not only increase an animal’s body mass, but also take up space in the

digestive tract that might otherwise have been used for nutritious materials (Lambert, 1999).

Therefore, we apply seed volume (henceforth referred to as seed size) as the parameter in the

subsequent study of size relationships between animals and seeds.

When possible, seed dimension data (length, breadth and/or width) were extracted from the

same papers as the data for animal × plant interactions, and from a few related papers (either

the publication series from the same authorships or from the same regions; Appendix S3).

Seed size was calculated as the volume of an ellipsoid, using the equation (seed size = (4/3) ×

π × (seed length/2) × (seed breadth/2) × (seed width/2)). If only two dimensions were

available, which were the most common cases, breadth and width were treated as equivalent

(seed size = (4/3) × π × (seed length/2) × (seed diameter/2)2). If only one dimension was

given, then we used the volume equation for a sphere (seed size = (4/3) × π × (seed

length/2)3). Where seed dimension could not be found, seed size was calculated from seed

mass using the equation from Moles et al. (2005) and using the data from the same source

Seed size and animal size

papers as for animal × plant interactions, from a few related papers (Appendix S3), or from

the Seed Information Database (SID) at the Royal Botanic Gardens, Kew (< 10% of the seed

size data). For species whose size was not obtained through any of the above methods,

nomenclatures were checked using the Taxonomic Name Resolution Service v3.2 (Boyle et

al., 2013), and the steps above were repeated using the accepted names.

The data set for the first two hypotheses (quantifying relationships between animal body

mass and ingested seed size) comprised 8800 unique animal × plant interactions, including

591 animal taxa and 3283 plant taxa. The animal species consisted of 232 mammals, 313

birds, 38 reptiles, 1 amphibian and 7 fish. The plant species were from 215 families and 1280

genera (Taxonomic Name Resolution Service v3.2). In this dataset, body masses of animals

spanned over six orders of magnitude, while the sizes of ingested seeds spanned over nine

orders of magnitude (Appendix S1). Balanites wilsoniana (c. 170000 mm3) was the largest

seed ingested by the largest animal (Loxodonta africana, African Elephant, 3900000 g), and

Gaultheria depressa (0.065 mm3) was the smallest seed ingested by the smallest animal

(Leiolopisma nigriplantare, Chatham Islands Skink, 3.3 g). The dataset can be accessed on

FigShare (Chen, 2015).

Data analysis

Body mass, seed size and number of ingested seed species were log10-transformed before analysis.

We used ordinary linear regression to quantify the relationship between body mass and ingested seed size, and applied a quadratic curve to allow for the possibility of a non-linear

Seed size and animal size

relationship. We used quantile regressions (focusing on the 95th and 5th quantiles) to quantify

the shape and strength of the relationship (Cade & Noon, 2003; Koenker, 2010).

The fruit type of each plant species was categorized as either fleshy or non-fleshy according

to the description from the source papers in Appendices S1 and S3, description from online

flora databases (such as Flora of Australia Online 2009), and from two professional

technicians (Yun-Hong Tan and Frank Hemmings). Fruits with conspicuous fleshy tissues

(e.g. pericarp, aril, thalamus, receptacle, calyx, rachis or bract or succulent pedicel) when

mature were classified as fleshy, and fruits without fleshy structures were classified as non-

fleshy (this definition is similar to that used by Willson et al. 1989). The data set for the fruit

type hypothesis comprised 5235 animal × plant interactions with fleshy fruits and 2449

animal × plant interactions with non-fleshy fruits. We performed ANCOVA to compare patterns of body mass and seed size with fleshy fruits vs. non-fleshy fruits.

Next we classified the animal species into large groups. Reptiles were subdivided into lizards

(Order Squamata) and tortoises (Order Testudines). Birds were subdivided into two groups

(Clements et al., 2013): flying birds (Infraclass Neognathae), flightless ratites and weak- flying tinamous (Infraclass Palaeognathae). Mammals were subdivided into (Wilson &

Reeder, 2005): marsupials (Infraclass Marsupialia), bats (Order Chiroptera), primates (Order

Primates), carnivores (Order Carnivora), even-toed ungulates (Order Artiodactyla), odd-toed ungulates (Order Perissodactyla), rodents (Order Rodentia), hares (Order Lagomorpha), and a few others (including two elephant species in the Order Proboscidea, Orycteropus afer in the

Order Tubulidentata, Euphractus sexcinctus in the Order Cingulata, and Erinaceus europaeus in the Order Erinaceomorpha). The treefrog, Xenohyla truncata, is the only fruit-eating amphibian in our dataset, and is the only frugivorous amphibian known in the world, feeding on five seed species (da Silva et al., 1989; da Silva & de Britto-Pereira, 2006). We excluded

Seed size and animal size

it and its diet from within-taxon analyses. We performed least square regression models for

each taxonomic group, as well as quantile regressions (95th and 5th quantiles) for groups with more than 200 interactions. Because of the scarcity of data in some categories, for groups that had 200 to 1000 interactions, we used a bootstrapping method to estimate standard errors in quantile regression, which did not make assumptions about the distribution of response variables (Koenker & Hallock, 2001). For groups with less than 200 interactions, only least square regressions were used. We performed ANCOVA to determine whether there were differences across the above taxa.

Most mammals were grouped as taxa with oral mastication, as they have fully-functioning grinding teeth. A few mammals that lack developed dentition [aardvark (Orycteropus afer), maned three-toed sloth (Bradypus torquatus), six-banded armadillo (Euphractus sexcinctus),

Southern three-banded armadillo (Tolypeutes matacus), Northern tamandua (Tamandua mexicana)], and birds, reptiles, amphibians and fish were grouped as taxa without oral mastication. Although these animals might use other structures to crush food (e.g. beaks in birds, pharyngeal teeth in fish), they lack effective oral chewing. Bats and flying birds were grouped as flying taxa (all of the bats in our dataset can fly). Birds in Infraclass Neognathae were regarded as flying birds, except those that are weak-flying or prefer to run rather than fly (Alectoris chukar, Fulica atra, Gallus gallus, Leipoa ocellata, Lophura diardi, Lophura nycthemera, Strigops habroptilus, Phasianus colchicus, Porphyrio mantelli). These terrestrial birds, together with the remaining taxa, including flightless ratites and weak-flying tinamous, were grouped as non-flying taxa. We applied ANCOVA to compare patterns of body mass and seed size across taxa with grinding teeth (mammals) vs. taxa without grinding teeth

(other taxa), flying taxa vs. non-flying taxa.

Seed size and animal size

Finally, we quantified the relationship between animal body mass and the number of ingested

seed species, using least square regression and quantile regression with bootstrapping method

to estimate standard errors (Koenker & Hallock, 2001).

All statistical analyses were performed using R version 3.0.3 (R Core Team, 2014) using the packages quantreg (Koenker, 2010) and ggplot2 (Wickham, 2009).

RESULTS

Larger animals ingest seeds with larger upper-bound sizes but not with larger average sizes

Contrary to expectations, there was a significant negative relationship between animal body mass and ingested seed size (Fig. 2; linear regression slope = -0.06, P < 0.0001, R2 = 0.003).

This relationship was significantly better described by a quadratic regression than by a linear regression (F = 636.65, P < 0.0001). The quadratic regression (Fig. 2; y ~ -0.20x2 + 1.30x -

0.48, P < 0.0001, R2 = 0.07) showed that the average size of ingested seeds decreased for

animals larger than 2 kg.

The upper-bound size of ingested seeds was positively related to animal body mass (for 95th

quantile, slope = 0.31, P < 0.0001), in line with our prediction. However, the lower-boundary

was significantly negative (for 5th quantile, slope = -0.17, P < 0.0001). That is, large animals

ingest seeds with a greater maximum size and a smaller minimum size than do small animals.

Seed size and animal size

Figure 2 The relationship between animal body mass (log10-transformed) and ingested seed size (log10-transformed). The solid line shows the least square regression. The dashed lines

show the quantile regressions (95th and 5th quantile). The long-dashed curve line shows the

quadratic least square regression.

Seed size and animal size

Figure 3 The relationships between animal body mass (log10-transformed) and ingested seed size (log10-transformed) across fruit types. The solid line shows the least square regressions.

The dashed lines show the quantile regressions (95th and 5th quantile). (a) seeds from fleshy fruits; (b) seeds from non-fleshy fruits.

Seed size and animal size

Larger animals ingest larger seeds from fleshy fruits but smaller seeds from non-fleshy fruits

There was a significant interaction between body mass and seed size across fruit types (P <

0.0001; Fig. 3). There was a significant positive relationship between animal body mass and the seed size of ingested fleshy fruits (Fig. 3; linear regression slope = 0.33, P < 0.0001, R2 =

0.09). Both the upper-bound size (for 95th quantile, slope = 0.44, P < 0.0001) and the lower-

bound size (for 5th quantile, slope = 0.11, P < 0.0001) of ingested fleshy-fruited seeds were

positively correlated with animal body mass. In contrast, the seed size of ingested non-fleshy

fruits was negatively correlated with animal body mass (Fig. 3; linear regression slope = -

0.28, P < 0.0001, R2 = 0.10). Both the upper-bound size (for 95th quantile, slope = -0.20, P <

0.0001) and the lower-bound size (for 5th quantile, slope = -0.14, P < 0.0001) of ingested seeds were negatively correlated with animal body mass.

Diverse scenarios in different groups

Different animal groups exhibited different correlations between body mass and ingested seed size (P < 0.0001 across groups of mammals, birds, reptiles and fish). There was no significant relationship between body mass and ingested seed size for fish (Fig. 4a). However,

the lack of a statistical significance may be due to the scarcity of ichthyochory data

empirically (Horn et al., 2011) and in our dataset (n = 7 species). There was a significant

positive relationship between reptile body mass and both the average and the upper-bound sizes of ingested seeds (Fig. 4b). This significant positive relationship was present in lizards but not within tortoises (Table 1). The entire avian group, as well as the two bird taxa (flying

Seed size and animal size

birds, ratites and tinamous), showed the expected positive relationship between body mass

and both the average and the upper-bound sizes of ingested seeds, but also a negative

relationship with the lower-bound sizes of seeds (Fig. 4c).

Contrary to expectations, mammals showed a significant negative relationship between body

mass and ingested seed size (Fig. 4d). Within mammals, most groups revealed significant

positive relationships with different levels of significance (bats, carnivores, marsupials, and

rodents). Primates and hares showed non-significant relationships, while even-toed ungulates

and odd-toed ungulates both showed significant negative relationships (Fig. 5 and Table 1).

The upper-bound sizes of ingested seeds were negatively related with body mass only in the

even-toed ungulates (Fig. 5 and Table 1), while positively related with body mass in other taxa (e.g. primates, bats, and carnivores).

There was a significant interaction between body mass and seed size across animals with and without grinding teeth (P < 0.0001 for the interaction; Appendix S4). The relationship between body mass and ingested seed size was significantly negative in animals with oral mastication (slope = -0.21, P < 0.0001, R2 = 0.02) while positive in animals without oral mastication (slope = 0.36, P < 0.0001, R2 = 0.06).

The relationships between animal body mass and ingested seed size significantly differed (P

< 0.0001 for the interaction; Appendix S4) across flying animals and non-flying animals.

Flying animals (slope = 0.69, P < 0.0001, R2 = 0.13) showed a tighter and opposite

relationship comparing with non-flying taxa (slope = -0.33, P < 0.0001, R2 = 0.05).

Seed size and animal size

Figure 4 The relationships between animal body mass (log10-transformed) and ingested seed size (log10-transformed) in four animal classes. The solid lines show the results of least

square regression. The dashed lines show the results of quantile regressions (95th and 5th

quantile) where sampling sizes are more than 200 animal × plant interactions. The black lines

represent slopes that are significantly different from zero, while the grey lines represent

slopes that were not significantly different from zero at an alpha of 0.01. (a) fish; (b) reptiles,

lizards in filled circles and tortoises in open triangles; (c) birds, flying birds in filled circles,

ratites and tinamous in open triangles; (d) mammals.

Seed size and animal size

Figure 5 The relationships between animal body mass (log10-transformed) and ingested seed size (log10-transformed) in mammalian groups. The solid lines show the results of least

square regression. The dashed lines show the results of quantile regressions (95th and 5th

quantile) where sampling sizes are more than 200 animal × plant interactions. The black lines

represent slopes that are significantly different from zero, while the grey lines represent

slopes that were not significantly different from zero at an alpha of 0.01. (a) even-toed ungulates; (b) odd-toed ungulates; (c) bats; (d) primates; (e) carnivores; (f) marsupials; (g) rodents; (h) hares.

Table 1 Regression coefficients and p-values for the animal × plant interactions in each group.

Group Interaction Least square regression Quantile regression number Slope (P) R2 5th Slope (P) 95th Slope (P) Mammal 5562 -0.21 (< 0.001) 0.02 -0.12 (< 0.001) 0.12 (< 0.001) Even-toed ungulate 1441 -0.86 (< 0.001) 0.10 -0.03 (0.77) -2.26 (< 0.001) Odd-toed ungulate 434 -2.25 (0.003) 0.02 -1.48 (0.03) -0.47 (0.74) Primate 2108 0.09 (0.05) 0.002 -0.05 (0.17) 0.26 (< 0.001) Bat 469 1.13 (< 0.001) 0.24 0.67 (< 0.001) 0.89 (< 0.001) Carnivore 559 0.25 (0.004) 0.02 -0.04 (0.64) 0.45 (0.003) Marsupial 115 0.58 (0.008) 0.06 1.02 0.47 Rodent 116 0.86 (< 0.001) 0.23 0.71 0.73 Hare 144 0.20 (0.58) 0.002 0.34 -0.68 Bird 2904 0.44 (< 0.001) 0.08 -0.23 (0.01) 0.68 (< 0.001) Neognathae 2726 0.57 (< 0.001) 0.09 -0.24 (0.03) 0.73 (< 0.001) Palaeognathae 178 0.64 (< 0.001) 0.13 -0.03 0.99 Reptile 238 0.22 (< 0.001) 0.07 -0.02 (0.81) 0.48 (< 0.001) Lizard 105 0.56 (< 0.001) 0.19 0.40 0.97 Tortoise 133 -0.24 (0.10) 0.02 -0.04 -0.53 Fish 93 0.66 (0.18) 0.02 1.51 0.82 Amphibian 3

Seed size and animal size

Larger animals ingest more species of seeds

Animal body mass and the number of seed species an animal ingests formed a positive

triangular relationship (Fig. 6; slope = 0.26, P < 0.0001, R2 = 0.28). Larger animals ingested seeds from a greater number of species (for 95th quantile, slope = 0.20, P < 0.0001). In our

dataset, the animal that ingested the most species of seeds was the South American Tapir

(Tapirus terrestris, c. 220 kg), which ingested 355 seed species (Fig. 6; O’Farrill et al., 2013).

But the lower-bound of the relationship between animal body mass and the number of ingested seed species was not significantly different to zero (for 5th quantile, P = 0.21).

Figure 6 The relationship between animal body mass (log10-transformed) and ingested seed diversity. The solid line shows the least square regression. The dashed line shows the 95th quantile regression.

Seed size and animal size

DISCUSSION

Our most important finding was the significant negative relationship between animal body mass and ingested seed size across all vertebrates. This finding is contrary to previous studies and assumptions, including opinion papers (e.g. Gautier-Hion et al., 1985; Jordano, 1995;

Izhaki, 2002; Eklöf et al., 2013), cross-species studies within same animal groups (e.g.

Heithaus et al., 1975; Wheelwright, 1985; Mack, 1993; Kalko et al., 1996; Lord, 2004; Cath

& Catterall, 2010; Burns, 2013) and community-wide empirical studies (e.g. Debussche &

Isenmann, 1989; Bollen et al., 2004; Donatti et al., 2011). Our within-taxon analyses and fruit type analyses shed light on the reason for this unexpected result. The negative relationship is largely due to the tendency of large animals, especially even- and odd-toed ungulates, to ingest small and dry seeds (Fig. 3; Fig. 5; Appendix S5). Ingestion of small seeds by large animals (such as ungulates, ratites and giant tortoises) has often been overlooked, but is in fact very frequent (Fig. 2). There are two main reasons why large animals might ingest small seeds. First, small seeds can be packed within large, nutritious fruits (e.g. some species in the genera of Ficus, Rubus) or ingested together with foliage

(Janzen, 1984). Second, larger animals may graze in grasslands or shrublands (Geist, 1974) where plants with small and dry seeds are common (Moles et al., 2005; Moles et al., 2007).

In this way, large animals could unintentionally ingest a large amount of small, dry and inconspicuous seeds that may survive through mastication and rumination (Janzen, 1984;

Chalukian et al., 2013), in contrast to small animals which are more likely to actively seek fleshy fruit rewards (Cath & Catterall, 2010).

This study advances our understanding of the relative importance of large versus small animals as seed dispersers. This is important as large animals are more likely to be classified as threatened than are small animals (Vidal et al., 2013; Dirzo et al., 2014), so the

Seed size and animal size

interactions between plants and large animals are more likely to become infrequent or be lost

entirely. Large animals ingest a higher diversity of seed species (Fig. 6), consistent with previous suggestions that the loss of large-bodied animals affects more plant species than does the loss of small-bodied animals (Wright et al., 2000; Terborgh et al., 2008). We also found a positive relationship between the upper-bound of seed sizes and animal body masses

(the 95th quantile in Fig. 2). This confirms the long-held idea that large-seeded plants are

particularly susceptible to the decline of large animals, as the largest seeds are only ingested

by the largest animals (Cramer et al., 2007; Markl et al., 2012; Galetti et al., 2013). However, our data also show that the smallest seeds ingested by large animals are even smaller than many of the seeds ingested by small and medium animals (the 5th quantile in Fig. 2 has a

significant negative slope). That is, if large animals become extinct in an ecosystem, it is not

only the largest-seeded species that lose their potential dispersers, but also some of the

smallest-seeded species. Nevertheless, the extinction of large animals being a risk to large-

seeded species and small-seeded species might be nuanced than at first it appears, as small

seeds tend to be ingested by multiple large animals (e.g. Bruun & Poschlod, 2006;

Jaroszewicz et al., 2013). That is, a greater redundancy in the potential disperser community

for small seeds may make small-seeded plants less vulnerable to the extinction of individual

large animal species. Our study is the first to show the negative lower boundary of the

relationship between animal body mass and ingested seed size. Most previous studies had

much smaller sample sizes than the present studies, and so may have lacked the statistical

power to distinguish between flat and negative lower boundaries (Wheelwright, 1985;

Debussche & Isenmann, 1989; Bollen et al., 2004; Burns, 2013). In addition, because large

animals are rare in communities and less dependent on fruits (Vidal et al., 2013), previous

quantifications of the relationship between animal body mass and ingested seed size focused

Seed size and animal size on small- and medium-sized frugivores (but see Gautier-Hion et al. 1985), thus missing the very large herbivores that drive the negative slope of 5th quantile we observed.

It is common practice in plant ecology to assume that small seeds with no specialized dispersal structures have unassisted dispersal (Hughes et al., 1994; Leishman & Westoby,

1994; Wang et al., 2012; Marteinsdóttir & Eriksson, 2013). Studies making this assumption would have incorrectly truncated the lower boundary of the relationship between animal body mass and ingested seed size. Our data compilation clearly shows that even very small seeds can be vertebrate dispersed (e.g. Juncus spp.). Thus, our study adds to a growing body of evidence (Myers et al., 2004; Bruun & Poschlod, 2006; Capece et al., 2013) suggesting that the practice of assuming frugivory and dispersal syndromes from seed size and morphology alone can lead to erroneous conclusions.

The negative relationship between animal body mass and ingested seed size casts doubt on the generality of optimal diet theory in shaping the relationship between frugivores and seeds.

Sih and Christensen (2001) concluded that optimal diet theory generally works well for foragers feeding on immobile prey. Our data are not consistent with this interpretation: the largest animals ingest both very small and very large seeds, suggesting that large animals are not necessarily seeking larger rewards proportionate to their body mass. Thus, while optimal diet theory may still hold in some cases, it does not seem to hold true across the spectrum of animal-seed interactions.

Our study is by far the most comprehensive synthesis of the relationships between the sizes of animals and the sizes of ingested seeds. However, we have only accounted the occurrence of interactions in which the animals ingest seeds. We have not measured dispersal quality or the intensity of animal-seed interactions. Animal body mass may affect other aspects of seed

Seed size and animal size

dispersal quality, such as the amount of seed carried, visitation rate, number of seeds

removed, and seed dispersal distance (Bruun & Poschlod, 2006; Markl et al., 2012; Wotton &

Kelly, 2012). Thus, an interesting and important direction for future work is to combine

information about animal body mass with the quantification of ingestion frequency, and/or

dispersal quality.

In summary, we have provided the first broad scale quantification of the relationship between

animal body mass and ingested seed size across mammals, birds, reptiles, amphibians and

fish. Our study shows that the common assumption that there is a positive relationship

between seed size and animal size only applies within a few taxa (e.g. birds), but is not a general rule in animal-seed interactions. Large animals are clearly important for both large-

seeded and small-seeded plants. These findings reshape and advance our knowledge of trait

matching relationships in animal-seed interactions and the roles of different animal taxa and

fruit types shaping animal-seed interactions.

ACKNOWLEDGEMENTS

We thank Britta K. Kunz, Aarón González-Castro (and IPNA-CSIC), Joel Strong, Aurélie

Albert, Jill Anderson, Ya-Fu Lee, and Nicole Gross-Camp for useful communication or

providing additional information on their publications. We thank Yun-Hong Tan and Frank

Hemmings for their valuable help on fruit type classification. We thank Jia-Jia Liu, Habacuc

Flores-Moreno and Julia Cooke for beneficial discussion and comments. S-C.C was supported by an UIPA scholarship from UNSW. A.T.M was supported by a QEII fellowship from the Australian Research Council (DP0984222). We thank David Currie, Brian Enquist

Seed size and animal size and Hans Henrik Bruun for providing insightful comments on previous versions of the manuscript.

DATA ACCESSIBILITY

The data used in this paper can be found at http://dx.doi.org/10.6084/m9.figshare.1428748

(Chen, 2015).

Drawing by Si-Chong Chen.

Seed size and animal size

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Seed size and animal size

SUPPORTING INFORMATION

Appendix S1 Overview of the collected animal × plant interactions and a list of literature

from which data were extracted.

Appendix S2 The relationships between animal body mass and ingested seed size using data

from faecal analyses.

Appendix S3 Additional sources of animal body masses and seed dimensions.

Appendix S4 The relationships between animal body mass and ingested seed size in (a) animals without oral mastication vs. animals with oral mastication; (b) flying animals vs. non-flying animals.

Appendix S5 The relationship between animal body mass and ingested seed size without ungulates.

Seed size and animal size

Appendix S1 Overview of the collected animal × plant interactions and a list of literature from which data were extracted.

group interaction source category animal taxa plant taxa number observation gut content faeces mixed n minimum maximum number* mammal 8589 2478 255 5607 637 234 Vampyressa thyone (7.17 g) Loxodonta africana (3900000 g) 4429 even-toed ungulate 1812 89 71 1611 60 37 Cephalophus monticola (6250 g) Giraffa camelopardalis (899995 g) 949 odd-toed ungulate 731 39 36 628 79 7 Tapirus pinchaque (154930 g) Diceros bicornis (1088225 g) 667 primate 3804 1991 97 1699 255 71 Microcebus rufus (42 g) Gorilla gorilla (124000 g) 2292 bat 669 219 0 433 53 50 Vampyressa thyone (7.17 g) Pteropus poliocephalus (825 g) 379 carnivore 758 31 23 664 80 28 Mustela itatsi (640 g) Melursus ursinus (136000 g) 511 marsupial 176 6 14 157 0 15 Gracilinanus agilis (22 g) Trichosurus vulpecula (2670 g) 141 rodent 214 85 14 14 101 13 Mus musculus (18 g) Agouti paca (10000 g) 192 hare 170 0 0 170 0 8 Sylvilagus bachmani (1095 g) Lepus arcticus (3850 g) 150 bird 4025 1812 330 1098 918 318 Dicaeum ignipectus (6.6 g) Struthio camelus (104000 g) 1802 neognathae 3634 1812 142 908 905 309 Dicaeum ignipectus (6.6 g) Pelecanus conspicillatus (8500 g) 1505 palaeognathae 391 0 188 190 13 9 Nothura darwinii (241 g) Struthio camelus (104000 g) 349 reptile 474 8 23 379 73 46 Leiolopisma nigriplantare (3.3 g) Chelonoidis nigra (250000 g) 376 lizard 197 1 10 141 49 32 Leiolopisma nigriplantare (3.3 g) Conolophus subcristatus (13000 g) 150 tortoise 277 7 13 238 24 14 Trachemys scripta (240 g) Chelonoidis nigra (250000 g) 247 fish 141 6 135 0 0 8 Ictalurus punctatus (833.5 g) Lithodoras dorsalis (10510 g) 107 amphibian 5 0 5 1 0 1 Xenohyla truncata (113 g) Xenohyla truncata (113 g) 5 Note: * including unknown plant species.

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Seed size and animal size

Appendix S2 The relationships between animal body mass and ingested seed size using data

from faecal analyses.

We performed additional analyses to confirm that the overall relationship was not artificially

strengthened or obscured by our inclusion of different data types. We used interactions

synthesized from faecal analyses, because seeds found in faeces are those that travelled through animal guts (i.e. very likely to be dispersed), and because data from faecal analyses were the most common type of data in our dataset (58%, 5053 out of 8776 unique interactions).

The relationship between body mass and seed size generated from faecal studies was consistent with the results based on the full dataset. There was a significantly negative relationship between animal body mass and the average size of seeds in faeces (slope = -0.09,

P < 0.0001, R2 = 0.007). As with the full dataset, the relationship based on faecal data was significantly better described by a quadratic regression (Fig. S1-1; y ~ -0.13x2 + 0.86x - 0.18,

P < 0.0001, R2 = 0.04) than by a linear regression (F = 179.88, P < 0.0001).

As with the full dataset, the faecal data showed that the upper-bound size of seeds was

positively related to body mass (for 95th quantile, slope = 0.23, P < 0.0001), while the lower- bound was negatively related to body mass (for 5th quantile, slope = -0.11, P < 0.0001).

Both the mean (slope = 0.25, P < 0.0001, R2 = 0.34) and upper-bound (for 95th quantile, slope

= 0.26, P < 0.0001) of the relationship between animal body mass and the number of ingested seed species were positive across data collected through faecal analysis (Fig. S1-2), indicating that more species of seeds travelled through larger animals’ digestive tracts. There was a non-significant relationship between animal body mass and the number of ingested

Seed size and animal size seed species at the lower boundary (for 5th quantile, P = 0.02). These results are consistent with our findings across the full dataset.

In summary, the main results of this study were consistent whether we included only faecal data, or included data from all possible data sources. That is, our results are neither obscured, nor artificially strengthened by our inclusion of the broadest possible dataset.

Seed size and animal size

Figure S1-1 The relationship between animal body mass (log10-transformed) and ingested seed size (log10-transformed) from faecal analyses. The solid line shows the least square

regression. The dashed lines show the quantile regressions (95th and 5th quantile). The long-

dashed curve line shows the quadratic least square regression.

Seed size and animal size

Figure S1-2 The relationship between animal body mass (log10-transformed) and ingested seed diversity from faecal analyses. The solid line shows the least square regression. The

dashed line shows the 95th quantile regression. Note that there is no faecal data for the fish

group.

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Appendix S3 Additional sources of animal body masses and seed dimensions.

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Appendix S4 The relationships between animal body mass (log10-transformed) and ingested seed size (log10-transformed) in (a) animals without oral mastication (circles and solid line) vs. animals with oral mastication (triangles and dashed line); (b) volant animals (circles and solid line) vs. non-volant animals (triangles and dashed line). The solid and dashed lines show the results of least square regressions.

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Appendix S5 The relationship between animal body mass and ingested seed size without

ungulates.

Ungulates were the main taxon driving the negative relationships seen across all vertebrates

and in mammals, as well as the pattern that large animals ingest seeds with a smaller

minimum size. If ungulates were excluded from the dataset, both the average (slope = 0.37, P

< 0.0001, R2 = 0.08) and upper-bound (95th quantile, slope = 0.48, P < 0.0001) of seed sizes

turned to be significantly positive, and the lower-boundary also changed to loosely positive

(5th quantile, slope = 0.11, P = 0.03; Fig. S5-1). However, the relationship between body

mass and seed size still decreased for animals larger than 11 kg (y~-0.15x2+1.23x-0.57, P <

0.0001, R2 = 0.09; Fig. S5-1).

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Figure S5-1 The relationship between animal body mass (log10-transformed) and ingested seed size (log10-transformed), excluding ungulates. The solid line shows the least square

regression. The dashed lines show the quantile regressions (95th and 5th quantile). The long-

dashed curve line shows the quadratic least square regression

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112

Chapter two

Plants show more flesh in the tropics:

variation in fruit type along latitudinal and climatic gradients

Accepted in Ecography

This chapter has been modified from the original paper specifically for this thesis

Si-Chong Chen1, William K. Cornwell1, Hong-Xiang Zhang1, 2 & Angela T. Moles1

1 – Evolution & Ecology Research Centre, School of Biological, Earth and Environmental

Sciences, University of New South Wales, Sydney, NSW 2052, Australia

2 – Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences,

Changchun, 130102, China

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Latitudinal and climatic gradients in fruit type

Abstract

Fruit type has a major impact on seed dispersal, seed predation and energy allocation, but our

understanding of large scale patterns in fruit type variation is weak. We used a dataset of

4008 Australian species to provide the first continental-scale tests of a series of hypotheses

about the factors that might affect fruit type. We found a significant latitudinal gradient in the

proportion of fleshy-fruited species, with the percentage of fleshy-fruited species rising from

19% at 43.75°S to 49% at 9.25°S. Species bearing fleshy fruits were more frequent on the

coastal fringes of Australia, while species bearing non-fleshy fruits became more frequent

toward the arid centre. Wet, warm and stable climates favoured fleshy-fruited species, with

the two best predictors of the proportion of fleshy-fruited species being maximum precipitation over five days (R2 = 0.40), and precipitation in the wettest month (R2 = 0.25).

The latitudinal gradient in fruit type shown here raises two interesting questions for future work: 1) Does the higher proportion of fleshy-fruited species at lower latitudes represent a latitudinal gradient in defence against pre-dispersal seed predators, since fleshy tissues could have a defensive role? 2) Do seeds achieve greater dispersal distances in the tropics, because fleshy fruits facilitate long dispersal distance by animal ingestion? Finally, our data are consistent with the idea that plant reproductive strategies are more often tied to conditions during the parts of the year in which they grow than to conditions during the harsh parts of the year.

Keywords: Fleshy fruit, dry fruit, precipitation, temperature, seasonality, endozoochory, seed dispersal

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INTRODUCTION

Plants make fruits with a wide range of forms (Seymour et al. 2013), from completely dry

(e.g. dandelions) to highly fleshy (e.g. raspberries). Whether a species’ fruits are fleshy or dry has a major impact on energy investment in fruits and the type of seed dispersal strategy the plant is likely to employ (Howe and Smallwood 1982), and subsequently on rates of pre- dispersal seed predation (Gautier-Hion et al. 1985) and seed dispersal distances (Thomson et al. 2011). The composition of fruit type varies across communities (Armesto et al. 2001,

Knight and Siegfried 1983, White 1994, Willson et al. 1989), but there is a surprising paucity of quantitative studies on the geographic distribution of plants bearing fleshy vs non-fleshy fruits. Some studies have suggested that abiotic factors, such as climatic variables, could play an important role in driving fruit types (Bollen et al. 2005, Bolmgren and Eriksson 2005,

Eriksson et al. 2000). However, most previous studies about the effect of climatic variables on fruit morphology have focused either on a few domesticated species because of their economic importance, or on intraspecific variation in fruit texture (see synthesis in Lotan and

Izhaki 2013). In this paper, we provide the first comprehensive quantification of how the proportion of plant species bearing fleshy fruits correlates with latitude and climatic variables at a broad scale.

We begin by quantifying the latitudinal gradient in fruit type. There are two potential reasons to predict that the proportion of fleshy-fruited species might decline with increasing latitude.

First, a higher proportion of fleshy-fruited species towards the tropics may be expected because seeds become larger towards the tropics (Moles et al. 2007), and fleshy fruits are generally associated with larger seeds (Bolmgren and Eriksson 2010, Butler et al. 2007, Chen et al. 2004, Willson et al. 1990). Several studies have surveyed the proportion of species bearing fleshy vs non-fleshy fruits within single communities (see synthesis in Willson et al.

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1989, Willson et al. 1990). However, quantitative analyses of broad-scale patterns in fruit

type are scarce (but see Almeida-Neto et al. 2008, Correa et al. 2015). These studies usually

took fleshy fruits as an indicator trait for endozoochory (i.e. internal seed dispersal by

animals), leaving the geographic pattern of intuitive fruit types still unclear. Second, one

traditional idea posits that biotic interactions are more intense at low latitudinal regions

(Schemske et al. 2009). If this was the case, we would expect that there would be a higher

proportion of species bearing fleshy fruits in the tropics because fleshy fruits are associated

with endozoochory (Howe and Smallwood 1982) and may act a defensive role against seed

predation (Mack 2000). However, recent data compilations have not supported the idea that

species interactions are stronger at low latitudes (HilleRisLambers et al. 2002, Moles and

Westoby 2003, Moles et al. 2011). Evidence about the latitudinal gradient in seed disperser activity is also inconclusive. Almeida-Neto et al. (2008) used fleshy fruits as an indicator trait for endozoochory and did not find a latitudinal trend in the percentage of vertebrate-dispersed species from 12.5°S to 25.5°S in Atlantic forest communities. However, because their study only focused on the tropical forest biome, the latitudinal gradient in fleshy-fruited species still needs more investigation.

As climate is one of the strongest drivers of latitudinal patterns in functional traits and biotic interactions (De Frenne et al. 2013), we then quantify the climatic gradients in fruit type.

Firstly, we hypothesize that there will be a higher proportion of fleshy-fruited species in areas with greater precipitation. This prediction is based on the fact that the bulky parenchymatous tissue of fleshy fruits accumulates water and organic compounds (Coombe 1976), and might be easier to produce when soil water potentials are close to zero and atmospheric demand for water is low. Consistent with this idea, plants produce fewer fleshy fruits in the dry season

(Bollen et al. 2005, Chen et al. 2004, Gliwicz 1987) or in extremely arid environments (Lotan

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Latitudinal and climatic gradients in fruit type and Izhaki 2013). Precipitation also impacts on the construction and maintenance of fleshy fruits (Debussche et al. 1987, Willson et al. 1989), probably by facilitating nutrient uptake in plants (Erskine et al. 1996, Nemani et al. 2003), and/or by influencing soil nutrients and moisture (Willson et al. 1989). For example, the water content of fleshy pulp of a desert fleshy fruit was positively correlated with soil water availability (Lotan and Izhaki 2013).

Although the idea that fleshy fruits tend to be produced in wet environments has intuitive appeal (Howe and Smallwood 1982, Vander Wall and Beck 2012), the relationship between precipitation and the frequency of fleshy fruits has not been rigorously quantified. For example, Butler et al. (2007) found the proportion of woody rainforest plants with fleshy fruits increases with precipitation, but their small study taxa and scale (24°S to 28°S in subtropical Australia) precludes a definitive conclusion on the effect of precipitation on fruit types. Further, we currently lack clear theory on which aspects of precipitation (e.g. mean precipitation or extremes) are most likely to be important in shaping the geographic distribution of fruit type. This study addresses these knowledge gaps.

Secondly, we hypothesize that climates with low minimum temperatures will have a lower proportion of species with fleshy fruits. This prediction is based on theory showing that succulent cells in fleshy fruits tend to form ice crystals at very low temperatures, leading to cold injury (Burke et al. 1976), whereas non-fleshy fruits are more adapted to freezing conditions (Chen et al. 2007, Vittoz et al. 2009). We also predict that climates with higher mean annual temperatures will have a higher proportion of fleshy-fruited species. In support of this prediction, it is estimated that over 70% of woody species bear fleshy fruits in the warm tropics (Willson et al. 1989), whereas the proportion of fleshy fruit is substantially lower (e.g. less than 10%) in alpine-nival regions in both the Northern (Chen et al. 2007,

Vittoz et al. 2009) and Southern Hemispheres (Willson et al. 1989).

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Latitudinal and climatic gradients in fruit type

Thirdly, we explore the relationship between the proportion of fleshy-fruited species and climatic variation. Fleshy fruits are expected to have greater tolerance to climatic variation

compared with non-fleshy fruits. Besides facilitating interactions with seed dispersing

frugivores (Willson et al. 1989), the structure of fleshy tissues is a mechanical and chemical

barrier to environmental changes (Beckman and Muller-Landau 2011, Coombe 1976, Hanley et al. 2007, Mack 2000). The parenchymatous tissues of fleshy fruits prevents seeds from dehydrating during periods of low water availability, while they do not change the persistence and dispersal of seeds during periods of excessive wetness (Seymour et al. 2013). In contrast, some non-fleshy fruits need consistent or sufficient dryness to release their seeds (White

1994). Thus, non-fleshy fruits might be more affected by climatic variation than are fleshy fruits. Correa et al. (2015) provided the only analysis that considers the effect of precipitation seasonality on the distribution of fruit types, and found that the proportion of endozoochory

(indicated by the proportion of fleshy-fruited species) increased with decreasing precipitation seasonality. However, they failed to consider other climatic variations and therefore came to an indefinite conclusion on the relationship between fruit type and climatic variation.

In summary, our main aims were to:

1. Quantify the shape and strength of the latitudinal gradient in interspecific fruit types;

2. Determine which climate variables are most tightly related with the proportion of

fleshy-fruited species;

3. Test specific hypotheses regarding the means, extremes and variations of precipitation

and temperature.

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Latitudinal and climatic gradients in fruit type

METHODS

A list of Australian plant species was obtained from the Flora of Australia Online database

(Flora of Australia Online 2009). Hybrids, aquatic species and naturalised species were

removed from the dataset. We checked the nomenclature against the Australian Plant Name

Index (Australian National Herbarium 2010). Outdated synonyms were corrected and

incorrect species with no clear synonyms were removed.

We categorized the fruit of each species as either fleshy or non-fleshy according to species

descriptions in the Flora of Australia Online database (Flora of Australia Online 2009). For

species that lacked fruit information in Flora of Australia Online, we referred to descriptions

in other sources, including FloraBase (Western Australian Herbarium 1998), PlantNET (The

Royal Botanic Gardens and Domain Trust 1999) and the database of Australian Tropical

Rainforest Plants (Centre for Australian National Biodiversity Research 2010). Fruits were

defined in a functional manner, as dispersal units, including true fruits (e.g. Beilschmiedia

recurva), accessory fruits (such as the receptacle in Semecarpus australiensis or the

receptacle in Podocarpus) and appendages (e.g. the sarcotesta in Cycads). Fruits were

classified as fleshy if they possessed conspicuous fleshy pericarps or fleshy appendages when

mature (e.g. aril, thalamus, receptacle, calyx, rachis or bract or succulent pedicel); otherwise,

they were classified as non-fleshy (this functional definition is similar to that used by Chen

and Moles 2015, Willson et al. 1989).

We obtained geo-referenced specimen records from the Atlas of Living Australia database

(Atlas of Living Australia, 2012). Only observations from mainland Australia and Tasmania

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Latitudinal and climatic gradients in fruit type

were retained. Records lacking species level identification and accurate locality were

excluded, as were records for hybrids and cultivars. The final dataset included 833,757

geographic occurrences for 4008 species (1236 with fleshy fruits, and 2772 with non-fleshy fruits). These species belong to 83 families and 369 genera.

We obtained data for 15 climatic variables (Supplementary material Appendix 1). Five temperature variables were extracted from the 2.5°×2.5° CRUTEM dataset (Jones et al. 2012), including mean annual temperature, the yearly and seasonal variance, the temperatures in the coldest and warmest months. Five precipitation variables were extracted from the 2.5°×2.5°

GPCC dataset (Schneider et al. 2011), including mean annual precipitation, the yearly and seasonal variance, the precipitation in the wettest and driest months. Five extremes indices were retrieved from the 3.75°×2.5° HadEX2 dataset (Donat et al. 2013), including maximum precipitation over five days (i.e. maximum consecutive 5 day precipitation), consecutive wet and dry days, frost days, and diurnal temperature range. All climatic data spanned the years

1951-2010.

The species list and their occurrence data were mapped onto 50 km grid cells using

Biodiverse v0.18 (Laffan et al. 2010). The count and proportion of fleshy-fruited species was exported for each grid cell. Our database included 2699 grid cells in total. The species number per grid cell ranges from 1 to 391, with a median of 27 species (Supplementary material Appendix 2). The species number per grid cell was more clustered around urban centres and roads due to sampling bias (Appendix 2). There are known, unavoidable biases associated with geographic data from the Atlas of Living Australia database (Atlas of Living

Australia, 2012), but work is being done to address these limitations, and any downsides are more than compensated by the advantage of a huge, geo-referenced dataset. Climatic variables were also imported into Biodiverse v0.18 (Laffan et al. 2010), and mapped onto 50

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Latitudinal and climatic gradients in fruit type

km grid cells in accordance with the species coordinates. The arithmetic mean value of each

variable was output for each grid cell.

The relationships between the proportion of fleshy-fruited species per grid cell and latitude and each climate variable were analysed separately, using logistic regression in R version

3.0.3 (R Core Team 2014). Logistic regression deals appropriately with the binary and bounded nature of proportion data (fleshy vs non-fleshy fruits), and weights grid cells according to the number of species they contain. The goodness-of-fit of each model was assessed by calculating McFadden's R2 (McFadden 1974), because it has the most intuitive interpretation and the widest application among pseudo-R2 analogues (Menard 2000). In

order to reduce type I error in multiple comparisons, Holm-Bonferroni correction (Holm

1979) was used to determine the statistical significance of all variables.

Pairwise correlations among the 15 climatic variables are shown in Supplementary material

Appendix 3. We aimed to select a parsimonious subset of climatic variables that explains

almost as much of the variation in the proportion of fleshy-fruited species as do all the

climatic variables (see Moles et al. 2009). We did a two-step model selection to estimate the

combined effect of climatic variables on fruit type. As the 32752 interaction terms that a fully

factorial model based on 15 variables would overwhelm the available degrees of freedom, we

started by reducing the initial pool of 15 variables to a smaller number according to the

results of univariate analyses. The four variables with R2 less than 0.02 (Appendix 1) were

excluded on the grounds that they could not substantially improve an overall model and they

may not tightly correlated with fruit type from a biological perspective. The remaining

variables were divided into three categories that characterize the key forms of climatic

variation described in our hypotheses – precipitation, temperature, and climatic variation.

Within each category, we selected the pair of variables that gave the model the highest R2, in

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Latitudinal and climatic gradients in fruit type order to reduce the effect of collinearity in climatic variables (e.g. Letten et al. 2013; see

Dormann et al. 2012). These selected six variables and their interactions (63 terms in total) were included in the initial multiple logistic regression. The final model was selected using

AIC-based backward elimination.

We performed principal component analysis (PCA; Gotelli and Ellison 2004) to investigate multivariate relationships among the full set of 15 climatic variables. As the variances of the variables were different, each variable was centre-scaled with a mean as zero and divided by its standard deviation. We used the principal components with cumulative variance more than

80% as the explanatory variables in the final logistic regression model (Gotelli and Ellison

2004).

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Latitudinal and climatic gradients in fruit type

Figure 1. Latitudinal gradient in the proportion of fleshy-fruited species. Top panel: The proportion of fleshy-fruited species in 50 km grid cells within Australia (9.25°S – 43.75°S).

Bottom panel: Geographic distribution of fleshy-fruited species in Australia. The graph was visualized according the proportion of fleshy-fruited species, but the analysis was based on the binary data of fleshy-fruited species vs non-fleshy-fruited species. The fitted line represents the predicted probabilities of fleshy-fruited species as fit by generalised linear models with binomial family and logit link. Cells with fewer than five species were removed for visual purpose, but all data were included in the analyses.

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Latitudinal and climatic gradients in fruit type

- ~ 1.00 (a) R1 = 0.389 (f) R1 = 0.154 (k)R1 = 0.114 c.Q) ]"' 0.75 .., ... ~ .. ..;: :=.. ,j :'; . . » . ~ . :i . ··.. · . ~ 0.50 i .!.... ~ t • Q) ""0 :5 0.25 t: c.0 ., . Ko .oo w ~ 1~ 1~ 4 6 8 10 0.5 1.0 maximum precipitation over 5 days (mm) consecutive wet days (day) seasonal precipitation variation (CV)

-"'~ 1·00 (b) R1 = 0.241 (g)" R1 = 0. 136 R1 = 0.013 c. "' ] 0.75 ·:; ..;: » ~ 0.50 Q) "" 0 :5 0.25 t: c.0 Ko .oo 0 100 200 300 400 8 10 12 14 0 10 20 30 total precipitation in wettest month (mm) diurnal temperature range (0 C) number of frost days (day)

- "'~ 1·00 (c) R1 = 0.238 (h) R1 = 0.126 { ip) R1 = 0.0 11 c. "' ] 0.75 ·:; ..;: » ~ 0.50 Q) "" 0 c: 0.25 ·-e0 ·: i: .: -~·~ a.0 12 0.00 c. 5 10 15 20 25 0.1 0.2 0.3 0 20 40 60 minimum temperature in coldest month {"C) seasonal temperature variation (CV) total precipitation in driest month (mm)

- ~ 1.00 (d) R1 = 0.204 (i) R1 = 0.118 (n) R1 = 0.002 c. "' ] 0.75 ·:; i ~ 0.50 Q) ""0 .§ 0.25 -e c.0 ~ 0.00 0.02 0.03 0.04 0.05 400 800 1200 1600 50 100 150 yearly temperature variation (CV) mean annual precipitation (mm) consecutive dry days (day) "' · ~ 1·00 (e)R1 = 0.20 1 G) R1 = 0. 11 6 (o) . R1 < 0.001 c. p = 0.84 "' .;·. ' ;·· ] 0.75 . -~: ·:; ... .:: >- ~ 0.50 Q) ""0 1!;{k;i;,')'f::J; . c: 0.25 .Q -e c.0 12 0.00 c. 10 15 20 25 10 15 20 25 30 0.2 0.4 0.6 0 mean annual temperature ( C) maximum temperature in hottest month (°C) yearly precipitation variation (CV)

124

Latitudinal and climatic gradients in fruit type

Figure 2. Logistic regressions of proportion of fleshy-fruited species per 50 km grid cell

within Australia with climatic variables. (a) to (o) are arranged from high to low McFadden's

R2 (see Supplementary material Appendix 1). The fitted lines display the predicted

probabilities of fleshy-fruited species as fit by generalised linear models with binomial family

and logit link, in which the climatic variables are significantly correlated with the proportion

of fleshy-fruited species (p < 0.0001). Graphs were visualized according the proportion of

fleshy-fruited species, but the analyses were based on the binary (fleshy-fruited species vs non-fleshy-fruited species) data. Cells with fewer than five species were removed for visual

purpose, but all data were included in the analyses.

RESULTS

There was a significant latitudinal gradient in the proportion of fleshy-fruited species in

Australia. The percentage of species bearing fleshy fruits increased from 19% at 43.75°S to

49% at 9.25°S (R2 = 0.26; Appendix 1). Species with fleshy fruits were more frequent along the east and north coastal fringes, and less frequent towards the central arid zones (Fig. 1).

The proportion of fleshy-fruited species was significantly correlated with most climate variables (p < 0.0001; Fig. 2 and Appendix 1), except for yearly precipitation variation (p =

0.84). Maximum precipitation over five days was the strongest correlate of the proportion of

fleshy-fruited species (R2 = 0.39), followed by total precipitation in wettest month (R2 = 0.24).

The temperature variable that was most closely correlated with the proportion of fleshy-

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Latitudinal and climatic gradients in fruit type

fruited species was minimum temperature in coldest month (R2 = 0.24). The measure of

climatic variation that was most closely correlated with the proportion of fleshy-fruited

species was yearly temperature variation (R2 = 0.20).

Using AIC-based stepwise regression, the final multiple logistic model did not drop any

variable or any interaction from the initial 63 terms (Fig. 2, Appendices 1and 3). This

combined model explained 67% of the variation in the proportion of fleshy-fruited species

(model p < 0.0001). In this model, the two precipitation variables that were most closely

correlated with the proportion of fleshy-fruited species were maximum precipitation over five

days (R2 = 0.39) and mean annual precipitation (R2 = 0.12). The two temperature variables

were mean annual temperature (R2 = 0.20) and maximum temperature in hottest month (R2 =

0.12), and the two measures of climatic variation were yearly temperature variation (R2 =

0.20) and diurnal temperature range (R2 = 0.14).

The PCA showed that the redundancy of climatic variables was low (Appendix 4). The first

axis of the PCA represented increasing consecutive dry days and decreasing yearly

temperature variation, explaining 37.45% of the variation. The second axis represented

increasing maximum precipitation over five days and total precipitation in driest month, and

decreasing consecutive dry days and seasonal temperature variation, explaining 32.71% of

the variation. The third axis represented increasing maximum precipitation over five days,

seasonal temperature variation, maximum temperature in hottest month, and consecutive wet

days, explaining 11.99% of the variation (Appendix 4). As the first three principal

components explained for 82.15% of the total variation in the 15 climatic variables

(Appendix 4), we included them as the explanatory variables in the final model. This

combined model explained 31% of the variation in the proportion of fleshy-fruited species.

There were significantly negative relationships between the proportion of fleshy-fruited

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Latitudinal and climatic gradients in fruit type

species and the first two principal components (slopes -0.06 and -0.08 respectively, p <

0.0001), and a significantly positive relationship between the proportion of fleshy-fruited species and PC3 (slope = 0.14, p < 0.0001).

DISCUSSION

Plant species are more likely to bear fleshy fruits towards the tropics, and more specifically, towards wet, warm environments with stable temperatures. This is consistent with some previous ideas (e.g. Vander Wall and Beck 2012, Willson et al. 1989), but ours is the first

comprehensive and quantitative evidence on the biogeography of fruit types. Only one

previous study had quantified the latitudinal gradient in fruit type, and it showed no

significant latitudinal trend in the proportion of fleshy-fruited species across 13° of latitude in

tropical forests (Almeida-Neto et al. 2008). In contrast, our study extended the quantification

to a continental scale spanning 34.5° latitude including both tropical and temperate biomes,

and confirmed our long-term impression on the distribution of fleshy fruits. As fruit type is a critical component of a species’ reproductive strategy, our result indicates that there is an important difference in plant reproductive strategy between the communities at low and high latitudes, as well as across different climatic regions. By quantitatively depicting the latitudinal trend in this key trait, our results open passageways to several ecological processes, such as seed dispersal and seed predation, as discussed below.

Fruit types are closely associated with seed dispersal modes (Howe and Smallwood 1982), and previous research has tended to equate fleshy fruits with endozoochory (e.g. Almeida-

Neto et al. 2008, Correa et al. 2015, Willson et al. 1989). Our results thus corroborate

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Latitudinal and climatic gradients in fruit type

previous evidence showing that the proportion of frugivore-dispersed taxa may increase

towards lower latitudes (Vander Wall and Beck 2012) and towards higher precipitation

(Almeida-Neto et al. 2008, Correa et al. 2015). Together with the fact that seeds dispersed by endozoochory go significantly further than do seeds dispersed by other vectors (Thomson et al. 2011), our finding raises the hypothesis that tropical species might achieve longer seed dispersal distances than species at higher latitudes. Longer seed dispersal distance could be the reason that the effects of distance and density dependent mortality (Connell 1971, Janzen

1970) have not been found to be stronger towards the tropics (HilleRisLambers et al. 2002).

However, the effects of fruit type on seed dispersal distance remain to be tested. We lack evidence at the moment on whether fleshy-fruited seeds are dispersed further than are dry- fruited seeds, especially considering that a large amount of dry-fruited species are also dispersed by endozoochory (Chapter one of this thesis).

In addition to facilitating dispersal, fleshy fruit also plays an important role in the defence against pre-dispersal seed predation (Beckman and Muller-Landau 2011, Mack 2000). The unripe pulp of fleshy fruit may provide a physical barrier against pre-dispersal seed predators

(Hanley et al. 2007), and/or contain a range of deterrent chemical compounds (Coombe 1976).

Field data on rates of pre-dispersal seed predation are consistent with this idea, with seeds in fleshy fruits receiving substantially lower rates of pre-dispersal seed predation than seeds in non-fleshy fruits in New South Wales, Australia (Moles et al. 2003), along the east coast of

Australia (Chapter four of this thesis), and in southern Spain (Herrera 1987). Thus, the latitudinal gradient in the proportion of fleshy-fruited species shown in the present study may represent a latitudinal gradient in defence against seed predators. This possibility merits further investigation.

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Latitudinal and climatic gradients in fruit type

On the surface, it seems that the substantially higher proportion of fleshy-fruited species in

the tropics is consistent with the idea that interactions between plants and animals are more

intense in the tropics (Schemske et al. 2009). High diversity of fleshy fruits in the tropics is assumed to raise intensive consumption from specialised fruit eaters which are generally absent from temperate latitudes (Snow 1971). However, a trend for more fleshy fruits towards the tropics does not necessarily mean that there will be a latitudinal gradient in the

frequency or importance of endozoochory towards the tropics, as there may not be a

latitudinal gradient in frugivore activity. Recent syntheses found that fruit removal, including

endozoochory (Schleuning et al. 2012) and seed predation (Chapter three of this thesis; Moles

and Westoby 2003), is not stronger or more specialized towards the tropics.

Previous studies have suggested an association between the frequency of fleshy-fruited

species and abiotic environment (Bollen et al. 2005, Howe and Smallwood 1982, Willson et

al. 1989), but were based on empirical observation at single communities or intuition. Our

findings directly verified previous hypothesis that abiotic factors play an important role in

shaping fruit type and help define fruit type patterns (Bolmgren and Eriksson 2005, Vázquez

and Stevens 2004, Willson et al. 1989). The proportion of variation in fruit type explained by

maximum precipitation over five days (40.2%) is amongst the highest for the relationship

between any plant trait and any single climatic variable. For instance, the strongest predictor

of plant height (precipitation in the wettest month) explained 25.6% of the variation (Moles et

al. 2009), the strongest predictor of leaf mass per area (irradiance) explained 17.6% of the

variation (Wright et al. 2005), and the strongest predictor of wood density (maximum

monthly temperature) explained 5.8% of the variation (Swenson and Enquist 2007). Given

that around half of the variation in most plant traits lies between coexisting species within

sites and thus cannot be explained by climate variables (Westoby et al. 2002), the fact that

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Latitudinal and climatic gradients in fruit type

maximum precipitation over five days explained 40% of the variation in fruit type is

remarkable. We do not currently have any theory that could explain why the relationship

between fruit type and maximum precipitation over five days is so much stronger than the

correlations for other precipitation variables, but further investigation of this pattern could

help us to understand the mechanisms underlying global patterns in fruit type. Improving our

understanding of which climate variables are most important in shaping ecological patterns is

critical if we are to accurately forecast the likely effects of climate change on our ecosystems.

Among the variables measuring averages, mean annual temperature was more strongly

correlated with the proportion of fleshy-fruited species than was mean annual precipitation

(Fig. 2 and Appendix 1), consistent with a meta-analysis showing that mean annual temperature is a better predictor of plant traits than is mean annual precipitation (Moles et al.

2014). This outcome is also in agreement with the relatively low proportion of fleshy-fruited species found in southeast Australia mainland and the island of Tasmania, where precipitation is sufficient to favour fleshy fruits but the temperature is relatively cool (Fig. 1).

However, our findings are not consistent with previous studies in the tropics (Almeida-Neto et al. 2008, Correa et al. 2015) in which the proportion of fleshy-fruited species was strongly positively correlated with annual mean precipitation but not correlated with temperature. This difference may arise because the temperature range covered by Almeida-Neto et al. (2008) was narrower than ours, making it harder to detect a pattern.

The correlations between fruit types and conditions during the harshest parts of the year (e.g. total precipitation in the driest month, R2 = 0.01) were substantially weaker than the

correlations between fruit types and environmental conditions during the parts of the year in

which they grow (such as maximum precipitation over five days, R2 = 0.39, total precipitation

in the wettest month, R2 = 0.24). This finding is consistent with studies of global patterns in

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Latitudinal and climatic gradients in fruit type

other plant traits, such as plant height (Moles et al. 2009) and wood density (Swenson and

Enquist 2007). In aggregate, these findings agree with Rohde (1992)’s suggestion that plant ecological strategies might be more strongly driven by conditions during the parts of the year in which they grow than by conditions in the harshest parts of the year. However, much of the theory about how climate shapes plant traits still relies on the idea that traits are affected largely by mortality during extreme events such as drought, heatwaves etc. (Allen et al. 2010,

Reyer et al. 2013). This is an area where future theory could be informed by large empirical

studies such as ours.

The range of the proportion of fleshy-fruited species in Australia (30% as mean, 0 to 60% as

95% interval, Fig. 1) is within the ranges of values reported for other parts of the world (0 to

over 70% in regions such as New Zealand, North America, the Neotropics and the

Mediterranean areas, according to the census in Willson et al. 1989 and Willson et al. 1990).

Although Australian ecosystems are not unusual in the spectra of fruit types (Willson et al.

1990), the patterns that we have found in Australia are open to be tested in other continents or

even at global scale.

In summary, we have demonstrated a latitudinal gradient in the proportion of fleshy-fruited species, which is substantially explained by the striking climatic gradients. Our results have implications for understanding latitudinal gradients in biotic interactions such as seed predation and seed dispersal, thus contributing to the current debate over latitudinal gradients in biotic interactions (Moles 2013, Schemske et al. 2009). We have also shed new light on the climatic factors that might drive large scale patterns in plant traits. Our findings lend support to the idea that plant traits are more strongly affected by conditions during parts of the year in which they grow than by conditions during the harshest parts of the year, suggesting that our current theories may focus on the wrong aspects of climate. Our work also provided

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Latitudinal and climatic gradients in fruit type empirical confirmation of many aspects of traditional theory such as that fruit types are the results of available resources. We still have much to learn about large-scale patterns in plant ecological strategies, especially for reproductive traits, but the current study has shed light on the factors that shape latitudinal gradients in fruit type, confirming some aspects of our traditional theory, but opening up many avenues for future research.

ACKNOWLEDGEMENTS

We thank Timothy Hitchcock, Shawn Laffan and Riin Tamme for valuable discussion and data information. We thank Floret Meredith for the inspiration of the witty title and beneficial discussion. S.-C. Chen was supported by an UIPA scholarship from UNSW and A. T. Moles was supported by a QEII fellowship from the Australian Research Council (DP0984222).

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SUPPLEMENTARY MATERIALS

Appendix 1. Logistic regressions of 15 climatic variables and latitude against the proportion of Australian fleshy-fruited species per 50 km grid cell.

Appendix 2. Histogram of studied species number per 50 km grid cell within Australia.

Appendix 3. The correlation matrix of the Pearson’s correlation coefficients between climatic variables.

Appendix 4. Principal component analyses of 15 climatic variables used in this study. Each factor represents an ordination axis.

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Appendix 1. Logistic regressions of 15 climatic variables and latitude against the proportion of Australian fleshy-fruited species per 50 km grid cell.

Variable β coefficient p-value McFadden's R2 maximum precipitation over five days (mm) 0.009 < 0.0001 0.389 total precipitation in wettest month (mm) 0.004 < 0.0001 0.241 minimum temperature in coldest month ( ) 0.070 < 0.0001 0.238 yearly temperature variation (CV) ℃ -62.353 < 0.0001 0.204 mean annual temperature ( ) 0.061 < 0.0001 0.201 consecutive wet days (days)℃ 0.226 < 0.0001 0.154 diurnal temperature range ( ) -0.124 < 0.0001 0.136 seasonal temperature variation℃ (CV) -3.256 < 0.0001 0.126 mean annual precipitation (mm) 0.001 < 0.0001 0.118 maximum temperature in hottest month ( ) 0.045 < 0.0001 0.116 seasonal precipitation variation (CV) ℃ 0.562 < 0.0001 0.114 frost days (days) -0.010 < 0.0001 0.013 total precipitation in driest month (mm) -0.004 < 0.0001 0.011 consecutive dry days (days) -0.001 < 0.0001 0.002 yearly precipitation variation (CV) -0.011 0.835 < 0.001

latitude 0.041 < 0.0001 0.260

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Appendix 2. Histogram of studied species number per 50 km grid cell within Australia. The embedded map shows the species number in 50 km grid cells.

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Appendix 3. The correlation matrix of the Pearson’s correlation coefficients between

climatic variables. Positive correlations are displayed in blue and negative correlations in red.

Colour intensity and circle size are proportional to the Pearson’s correlation coefficients. The legend on the right side of the correlogram shows the correlation coefficients and the corresponding colours.

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Appendix 4. Principal component analyses of 15 climatic variables used in this study. Each principal component represents an ordination axis. Values in bold are significant (p < 0.0001).

Variable PC1 PC2 PC3 maximum precipitation over five days (mm) 0.322 0.496 0.697 total precipitation in wettest month (mm) 0.311 0.885 0.089 minimum temperature in coldest month ( ) 0.903 0.292 -0.156 yearly temperature variation (CV) ℃ -0.605 -0.305 0.284 mean annual temperature ( ) 0.931 -0.051 0.295 consecutive wet days (days)℃ 0 0.787 0.404 diurnal temperature range ( ) -0.270 -0.820 0.146 seasonal temperature variation℃ (CV) -0.311 -0.532 0.662 mean annual precipitation (mm) -0.151 0.932 0.103 maximum temperature in hottest month ( ) 0.774 -0.303 0.507 seasonal precipitation variation (CV) ℃ 0.817 0.378 -0.073 frost days (days) -0.785 0.280 0.240 total precipitation in driest month (mm) -0.828 0.405 0.160 consecutive dry days (days) 0.675 -0.484 -0.151 yearly precipitation variation (CV) 0.391 -0.735 0.332

eigenvalue 5.618 4.907 1.798 variance (%) 37.450 32.710 11.986 cumulative variance (%) 37.450 70.161 82.146

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144

Chapter three

Is there a latitudinal gradient in seed predation?

Formatted for submission to Ecology Letters

Si-Chong Chen1, Frank A. Hemmings2, Fang Chen3 & Angela T. Moles1

1 – Evolution & Ecology Research Centre, School of Biological, Earth and Environmental

Sciences, University of New South Wales, Sydney, NSW 2052, Australia

2 – John T. Waterhouse Herbarium, School of Biological, Earth and Environmental Sciences,

UNSW Australia, Sydney, NSW 2052, Australia

3 – Department of Biochemistry and Molecular Biology, School of Basic Medical Science,

Wuhan University, Wuhan, China

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Abstract

We quantified seed predation across 25 sites spanning 28° of latitude on the east coast of

Australia, to test the hypothesis that plants lose a higher proportion of seeds to seed predation

at lower latitudes. Contrary to expectations, pre-dispersal seed predation increased with latitude across 256 species-site combinations. There was no significant relationship between

latitude and post-dispersal seed removal, either across 126 species-site combinations or within species. The removal of standard rice grains was significantly higher at lower latitudes, suggesting that naturally collected seeds at lower latitudes may possess greater defenses to reduce predation. Our findings are not consistent with the hypothesis that the striking diversity of plant species in tropical habitats is caused by stronger herbivore pressure in the tropics. This study, combined with previous studies of biotic interactions, highlight an urgent need for new theories for understanding global patterns in biotic interactions.

Keywords: granivory, herbivory, seed removal, biotic interaction, biodiversity

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INTRODUCTION

It has long been accepted that biotic interactions are more intense at lower latitudes (Wallace

1878; Dobzhansky 1950; Coley & Aide 1991; Schemske et al. 2009). However, recent syntheses and meta-analyses have found that herbivory (Moles et al. 2011; Poore et al. 2012;

Kozlov et al. 2015), distance and/or density dependent mortality (HilleRisLambers et al.

2002; Hyatt et al. 2003; Comita et al. 2014), pollination (Ollerton & Cranmer 2002; Vamosi et al. 2006; Schleuning et al. 2012) and frugivory (Almeida-Neto et al. 2008; Schleuning et al. 2012) are not stronger or more specialized towards the tropics (Ollerton 2012; Moles

2013). We established a latitudinal transect including 25 sites arrayed along the east coast of

Australia (Fig. 1 and Appendix S1), spanning 28 degrees of latitude from Far North

Queensland (15°30'S) to southern Tasmania (43°35'S), to determine whether there is a latitudinal gradient in seed predation.

Losses due to seed predation have a profound effect on the reproductive success of adult plants (Hanley et al. 2007), and can result in dramatically reduced seedling recruitment

(Janzen 1971). There are two categories of seed predation: pre-dispersal seed predation, which happens when the seeds are still attached to their mother plant and is largely carried out by invertebrates (Kolb et al. 2007), and post-dispersal seed predation, which happens when the seeds have dispersed away from their parents and is carried out by both vertebrates and invertebrates (Hulme 1998). It has been estimated that plants lose an average of 45% of their seeds to pre-dispersal predation, and 50% of the remaining seeds to post-dispersal predation (Crawley 1992).

We hypothesize that seed predation is more intense towards lower latitudes. Geographic patterns in herbivory on seeds have received considerably less attention than has herbivory on

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Latitudinal gradient in seed predation leaves (see the literature synthesized in Moles et al. 2011). However, seeds are the most vulnerable stage in a plant’s life cycle (Hanley et al. 2007), and seed herbivory may have different patterns to leaf herbivory (Anstett et al. 2014; Moreira et al. 2015). Therefore, we aim to provide the first geographically and taxonomically large experiment on the latitudinal gradients in pre-dispersal seed predation and post-dispersal seed removal at both cross- species and within-species levels.

Hypothesis 1: Plants lose a higher proportion of their seeds to pre-dispersal seed predation at lower latitudes at the cross-species level.

There has only been one previous study of the latitudinal gradient in pre-dispersal seed predation at the cross-species level. This study compiled data for 122 species from the published literature, and found no significant latitudinal gradient in pre-dispersal seed predation (Moles & Westoby 2003). Although data syntheses provide a powerful tool to summarize the latitudinal pattern in seed predation, their interpretation is complicated by the inconsistent protocols of the synthesized studies (Orrock et al. 2015). Here, we aim to quantify seed predation using a consistent field-based protocol across broad taxonomic and geographic ranges. This approach is powerful, as it measures actual predation rates on wild seeds in their natural habitats. That is, it gives an indication of the actual level of seed predation experienced in the real world.

Hypothesis 2: Plants lose a higher proportion of their seeds to pre-dispersal seed predation at lower latitudes at the within-species level.

Previous studies of the relationship between pre-dispersal seed predation and latitude have found positive (Toju & Sota 2006; Anstett et al. 2014; Moreira et al. 2015), negative

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(Verhoeven & Biere 2013; Kambo & Kotanen 2014), or no relationship (García et al. 2000;

Alexander et al. 2007; Montesinos et al. 2010; Lee & Kotanen 2015). These empirical studies

were all on single species, but no statistical synthesis can be made due to the scarcity of such

studies. We aim to examine a wide range of species using a consistent protocol, to determine if low-latitude populations suffer more intense than do high-latitude populations.

Hypothesis 3: Plants lose a higher proportion of their seeds to post-dispersal seed removal at lower latitudes at the cross-species level.

Three data syntheses have investigated latitudinal gradients in post-dispersal seed removal at the cross-species level. A literature compilation found no significant latitudinal gradient in post-dispersal seed removal across 205 species (Moles & Westoby 2003). Hulme and

Kollmann (2005) re-analyzed Moles and Westoby (2003)’s compiled data set using a discontinuous approach where the latitudinal gradient was categorized into binary zonobiomes, and found that post-dispersal seed removal was lower in the tropics than in the temperate zone. Finally, a synthesis found that seed removal by invertebrates was higher

toward the tropics, whereas seed removal by vertebrates was higher toward the poles,

explaining the lack of latitudinal gradient in total post-dispersal seed removal (Peco et al.

2014). Here, we provide the first empirical quantification at the cross-species level of the latitudinal gradient in post-dispersal seed removal.

Hypothesis 4: Plants lose a higher proportion of their seeds to post-dispersal seed removal at lower latitudes at the within-species level.

Little work has been done to examine the latitudinal gradient in post-dispersal seed removal at the within-species level. The only study compiled data spanning five sites, and found that

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European yew (Taxus baccata) received lower post-dispersal seed removal at intermediate latitudes (Sanz & Pulido 2014). How post-dispersal seed removal changes with latitude for populations of other species is stunningly unknown, and we aim to fill this knowledge gap by within-species studies applying a consistent protocol.

Hypothesis 5: The proportion of standard seeds lost due to post-dispersal seed removal is higher at lower latitudes.

Local seeds may have characteristics unique to each site that could confound comparisons across sites. For example, seeds at low latitudes are posited to be better defended (Coley &

Kursar 2014). In contrast, standard seeds possess consistent defenses against predation, and thus complement information on seed traits in seed-predator interactions. Orrock et al. (2015) provide the only large-scale study on seed removal of standard seeds (oat grains) across eleven grassland sites spanning 15° of temperate latitude. Although they did not analyze the latitudinal variation in seed removal (because their study had other aims), an analysis of their data revealed that a marginally higher proportion of oat grains were removed at lower latitudes (linear regression with logit-transformed proportion, p = 0.07). It is unclear whether this weak gradient becomes significant or vanishes across both tropical and temperate latitudes. Thus, in addition to using natural seeds in their natural habitats, we also quantify the latitudinal gradient in post-dispersal seed removal with standard seeds (rice grains).

MATERIALS AND METHODS

Site location and species selection

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We established a latitudinal transect including 25 sites arrayed along the east coast of

Australia (Fig. 1 and Appendix S1), spanning 28 degrees of latitude from Far North

Queensland (15°30'S) to southern Tasmania (43°35'S). These sites were all in protected low- altitude (mostly under 200 meters a.s.l., but two sites at 308 meters and 347 meters respectively), in sclerophyll vegetation, within 50 kilometers of the coast. The fieldwork was done in early 2015 (late summer, which is generally the peak fruiting season in Australia).

We did not simply travel from the south to the north, but arranged sampling dates such that latitude and time of sampling were not confounded (Appendix S1).

To be included, a species had to present a minimum of 50 mature fruits from at least five parent plants. At each site, we spent half a day on seed collection, and measured pre-dispersal seed predation and post-dispersal seed removal within the following two days, using the field procedure from Moles et al. (2003).

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Figure 1 Pre-dispersal seed predation and post-dispersal seed removal were measured at 25

sites along the east coast of Australia. For a detailed description of field sites, see Appendix

S1 in Supporting Information.

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Pre-dispersal seed predation

For species used in the study of pre-dispersal seed predation, we measured a minimum of 50 mature seeds from at least five individuals per species per site, but increased the number to

100 seeds per species whenever possible. This resulted in pre-dispersal seed predation data for 256 species-site combinations (170 unique species; Appendix S2).

Seeds were opened and checked for damage on site, immediately after collection. Seeds were considered to have been depredated if presenting clear evidence of damage (e.g. entry/exit holes, invertebrates, frass, or fragments of damaged seed coat). Aborted seeds were not included in counts. Predation rates for species with large numbers of small seeds per fruit

(such as Leptospermum, Eucalyptus, Melaleuca, and Kunzea species) were estimated based on the intactness of fruits.

Post-dispersal seed removal

For species used in the study of post-dispersal seed removal, a minimum of 100 intact seeds from at least five individuals per species were collected. Seed structures that were usually lost during seed dispersal, such as fleshy pericarps and elaiosomes, were entirely removed.

Species with very small seeds that might be hard to distinguish and thus relocate on the soil surface were excluded from this part of the study. Because of this criteria and the seed number requirement, the species used in the study of post-dispersal seed removal was a subset of the species used in the study of pre-dispersal seed predation. Overall, we collected

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post-dispersal seed removal data for 126 species-site combinations (91 unique species;

Appendix S3).

Twenty depots were established for each species at each site. Depots were established at five meter intervals along transect lines at the same site where the seeds were collected. Marking tape was placed a meter away from the depots at the beginning and ending of transect lines, but used as little as possible for depots in the middle parts of the transect lines to minimize any potential influence on seed predator behavior. Seed depots were designed to mimic the natural situation as much as possible, but meanwhile allow us to revisit them in dense forests.

At each depot, five intact seeds of a single species were deposited directly on the soil surface,

within five centimeters of a wooden barbecue skewer. Each depot was established in shallow

depressions on bare ground (made by treading the heel of a boot in to the ground), which

minimized the chances that seeds would be blown away by wind. Twenty-four hours after

they were established, and the number of intact seeds remaining at each depot was recorded.

Seeds were considered to have been removed if they presented clear evidence of predation, or

if they could not be located within 30 cm of the barbecue skewer.

We measured post-dispersal seed removal rather than actually quantifying seed predation, as have many other studies (see Orrock et al. 2015). The fate of the seeds could not be easily determined but we suspect that most animals do not have good intentions when they remove seeds. Although it is widely acknowledged that seed removal plays an important role in secondary seed dispersal (Vander Wall et al. 2005), survival from removed seeds is actually very low (Hulme 1998; Hillyer & Silman 2010).

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Removal of rice grains

Brown rice grains (Oryza sativa; sterilized using 33 kGray gamma irradiation to prevent germination) were used to provide standardized measurements of post-dispersal seed removal

across all latitudes. The brown rice grains were set out without husks, thus presenting low

and consistent defense levels across latitudes. The removal of brown rice grains was

measured at the same time that we quantified post-dispersal seed removal on field-collected

seeds. We measured removal of rice grains at all but one of our 25 study sites (the exception

was Royal National Park at 34°05’S, where pre-dispersal seed predation and post-dispersal

seed removal on field-collected seeds were quantified before we had finalized the design of

the standard rice grain trials). Twenty depots, each containing five brown rice grains, were

established at each site and were censused after 24 hours, using the same protocol as for the

field-collected seeds.

Data analysis

Because of the binary (seeds predated vs survived) and bounded (seed predation ranges from

0% to 100%) nature of seed predation data, we analyzed the data using logistic regression.

Logistic regressions deals with the mean–variance relationship of the binomial data structure,

and also weights each species-site combination according to the integer counts it contains

(Peng et al. 2002). For the cross-species relationships between latitude and pre-dispersal

predation and post-dispersal removal on field-collected seeds, we used a mixed effects

logistic regression model with a fixed-effect term for latitude and a random-effect term for

site. The random effect for site accounts for the non-independence of data points gathered at

the same time and in the same location. Mixed effects logistic regression models were fitted

using restricted maximum likelihood via the glmer function in the lme4 package (Bates et al.

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2014) in R version 3.0.3 (R Core Team 2014). P-values were obtained by likelihood ratio

tests of the full model with latitude against the null model without latitude (Bolker et al.

2009).

The vast majority of the field-collected species are native to Australia, but a few naturalized

species were included (six species-site combinations for pre-dispersal seed predation and two

species-site combinations for post-dispersal seed predation). The results of our main analyses

were qualitatively similar when done including all data from both native and naturalized

species, and when performed using just those data from native species. Therefore, we have

presented the results of analysis including both native and naturalized species throughout the

rest of the paper, as these analyses have the greatest statistical strength and taxonomic

breadth.

To quantify the latitudinal gradients in predation and/or removal at the within-species levels,

we used within-study meta-analyses (e.g. van Zandt 2007; van Kleunen et al. 2011) for field-

collected seed species that were sampled at more than one site. We used the log odds ratio

[ln(OR)] from 2 × 2 contingency tables as the measure of effect size (Borenstein et al. 2009).

The odds ratio measures the binary difference between the number of events (seed predation)

and non-events (seed survival) in two groups (the highest latitude site at which the species

was sampled vs the lowest latitude site at which the species was sampled; Appendix S4). This

yielded 43 effect sizes for pre-dispersal seed predation and 22 effect sizes for post-dispersal

seed removal. The tests of total heterogeneity were not significant (Qtotal = 26.426, p = 0.971 for pre-dispersal seed predation; Qtotal = 20.179, p = 0.510 for post-dispersal seed removal),

suggesting no outlier of the effect sizes (Rosenberg et al. 2000). We used the software

package MetaWin version 2.0 (Rosenberg et al. 2000) to calculate effect sizes and 95%

bootstrapped confidence intervals (CI) of the mean effect size based on 1000 iterations. The

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meta-analyses were performed using random effects models, assuming that the studies of

each species differed not only in sampling error but also by a random component of their

effect sizes. The latitudinal difference between the northernmost site and the southernmost

site for each species was taken as a continuous independent variable in the meta-analytic model, in order to evaluate whether the variability in effect sizes could be explained by the latitudinal difference. The relationship between effect size and the latitudinal difference was determined using weighted least squares regression (Rosenberg et al. 2000). The latitudinal difference between the paired sites did not have an effect on the effect sizes of within-species comparisons for either pre-dispersal seed predation (p = 0.151) or post-dispersal seed removal (p = 0.927; Appendix S5). Differences between seed predation values at each end of the latitude range were evident when the CIs did not overlap zero: positive values of effect size implied that seed predation was higher at high latitude, while negative values implied that seed predation was higher at low latitude.

The relationship between latitude and rice grain removal was analyzed using logistic regression.

RESULTS

Pre-dispersal seed predation was more intense towards high latitudes at the cross-species level

There was a latitudinal gradient in pre-dispersal seed predation at the cross-species level, with lower predation towards low latitudes (β coefficient = 0.060, χ2 = 5.518, p = 0.019). The

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Latitudinal gradient in seed predation

fitted model predicted that pre-dispersal seed predation decreased from 12.6% at the highest

latitude (43°35'S) to 0.03% at the lowest latitude (15°30'S; Fig. 2a). Pre-dispersal seed

predation was generally low across all latitudes, with 110 out of 256 species-site

combinations presenting zero predation (Appendix S2). The highest level of pre-dispersal

seed predation was 85.5%, experienced by Bursaria spinosa at 37°17’S.

Pre-dispersal seed predation showed no latitudinal gradient at the within-species level

Among the 43 species for which we had pre-dispersal seed predation data at more than one latitude (Appendix S2), 13 species showed higher pre-dispersal seed predation at lower latitudes, 16 showed no significant relationship between pre-dispersal seed predation and

latitude, and 14 showed higher pre-dispersal seed predation at higher latitudes. That is, only

30% of the within-species comparisons were consistent with our hypothesis, showing higher

rates of pre-dispersal seed predation at lower latitudes. The average effect size was not

significantly different from zero (average log odds ratio = 0.217; 95% bootstrapped CI = -

0.179 to 0.609). That is, the within-species data do not support the idea that pre-dispersal

seed predation is more intense at lower latitudes.

Post-dispersal seed removal showed no latitudinal gradient at the cross-species level

There was no significant latitudinal gradient in post-dispersal seed removal at the cross-

species level (χ2 = 2.264, p = 0.132; Fig. 2b). Post-dispersal seed removal ranged from 0 to

100%, with a median of 41% and a mean of 45% (Appendix S3).

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Latitudinal gradient in seed predation

Figure 2 The proportion of seed predation/removal across latitudinal gradient. (a) pre-

dispersal seed predation; (b) post-dispersal seed removal after 24 hours of exposure to post-

dispersal seed predators; (c) removal of brown rice grains after 24 hours of exposure to post-

dispersal seed predators. Graphs are visualized using the proportions of seed

predation/removal for the ease of display purpose, but the analyses are based on the binary

data of predated/removed vs survived seeds. Each point represents the mean proportion of

seed predation/removal for one species-site combination. The curves denote mixed effects

logistic regression (a and b) or logistic regression (c) of the proportion of seed

predation/removal over latitude. Points on (a) are jittered to reduce overplotting.

Post-dispersal seed removal showed no latitudinal gradient at the within-species level

There were 22 species for which we had post-dispersal seed removal data from more than one

latitude (Appendix S3). Fourteen of these species showed higher post-dispersal seed predation at lower latitudes, and eight showed higher post-dispersal seed predation at higher latitudes. The meta-analysis on the within-species comparisons showed that the average effect size was not significantly different from zero (average log odds ratio = -0.449; 95% bootstrapped CI = -1.037 to 0.127). That is, the within-species data do not support the idea that post-dispersal seed predation is more intense at lower latitudes.

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Removal of rice grains was more intense towards low latitudes

Removal of rice grains was significantly higher towards lower latitudes (β coefficient = -

0.064, p < 0.001). The fitted model predicted that the removal of rice grains increased from

59.9% at the highest latitude (43°35'S) to 90.1% at the lowest latitude (15°30'S; Fig. 2c).

DISCUSSION

Our study provides the most concrete evidence to date that seed predation on natural seeds is

not more intense towards the tropics. Pre-dispersal seed predation is higher towards higher

latitudes at the cross-species level, and shows no latitudinal gradient at the within-species

level. There is no significant latitudinal gradient in post-dispersal seed removal at either the

cross-species or the within-species level. Our empirical study of seed predation on natural

seeds adds to a growing body of literature (HilleRisLambers et al. 2002; Ollerton & Cranmer

2002; Moles & Westoby 2003; Almeida-Neto et al. 2008; Moles et al. 2011; Poore et al.

2012; Schleuning et al. 2012), casting doubt on the generality of the idea that biotic

interactions are more intense at lower latitudes, and reinforcing recent suggestions that our

understanding of the factors shaping global patterns in ecology needs reassessing (Moles &

Ollerton 2016).

The idea that there is a greater intensity of biotic interactions in the tropics is the cornerstone

that underpins many theories seeking to explain the latitudinal gradient in biodiversity (Coley

& Kursar 2014). Clearly, our results are of importance to this body of literature. The

phenomenon of declining interaction intensity with distance from the equator may be

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plausible for some biotic interactions, but recent evidence suggests that it is neither a general

phenomenon across communities nor an explanatory mechanism for the gradient in

biodiversity (Moles & Ollerton 2016).

The Janzen-Connell hypothesis (Janzen 1970; Connell 1971) proposed that higher levels of

density- and distance-dependent mortality in the tropics might promote and maintain the high

plant diversity in tropical communities. However, three recent data compilations have found

no latitudinal gradient in the strength of either distance- or density-dependent mortality

(HilleRisLambers et al. 2002; Hyatt et al. 2003; Comita et al. 2014). These results are consistent with our finding that interactions between plants and their pests (seed predators and herbivores) are not more intense towards the tropics. It is possible that a high intensity of pest pressure (see Coley & Kursar 2014) may not be solely responsible for maintaining high tropical diversity. Other factors could also promote the sympatric coexistence of tropical plants (reviewed in Wright 2002), such as fungal pathogens (Bagchi et al. 2014), variable mating behaviors (Cannon & Lerdau 2015), or microhabitat specialization (Leigh et al. 2004).

One of the arguments for the latitudinal gradient in biotic interactions was that the higher

species richness in tropical habitats might have led to higher levels of biotic interactions

(Dobzhansky 1950). It is posited that a great diversity of seed predators occurs in the tropics

(Hulme 1998). However, it is not the diversity of seed predators that is most important for the

plants, but rather the proportion of seeds destroyed by seed predators. Even a single species

of seed predator can be devastatingly effective. For the European beech as an example, up to

55% of seeds are predated by a single species of seed worm in pre-dispersal phrase (Nielsen

1977), while outbreaks of bank voles can consume up to 100% of their available seeds in

post-dispersal phrase (Jensen 1982).

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The result based on rice grains supplements the results based on natural seeds, as the later

ones inform the actual level of seed predation experienced in the real world. The difference

between our results for rice grains (which showed higher post-dispersal removal at low latitudes), and for the naturally occurring species (which showed no significant latitudinal gradient in post-dispersal seed removal) is consistent with the idea that a co-evolutionary arms race (Coley & Kursar 2014) may have led seeds at low latitudes to be better defended than seeds at high latitudes. Seed reserves of field-collected species are packed in seed coats

(post-dispersal) and/or together in pericarps (pre-dispersal) that afford mechanical and/or chemical resistance against predators (Hanley et al. 2007). Several distinct defensive structures were prevalently found in our studied species at northern Queensland sites (tropical areas): the berries of the stinging tree Dendrocnide moroides and the follicles of the snake

vine Hibbertia scandens were covered by irritating hairs; the pods of Brachychiton bidwillii

were highly pubescent; several species presented latex in their fruits (Tabernaemontana pandacaqui, Alyxia ruscifolia and Alyxia spicata). Moreover, we found an increasing proportion of fleshy-fruited species towards the tropics (logistic regression β coefficient = -

0.078, p < 0.0001; also see Chapter two of this thesis) where the fleshy pericarps are

proposed to be a barrier against pre-dispersal seed predators (Mack 2000; Hanley et al. 2007).

However, species presenting spiky or pubescent diaspores (such as Isopogon species and

some grass seeds) were equivalently pervasive across latitudes. In addition to seed structural

defense, chemical resistance and nutrient investment may also vary along latitudes, as

indicated by some intraspecific studies (e.g. De Frenne et al. 2011). The understanding of latitudinal gradients in seed defense and seed nutrition is a striking knowledge gap that lack concrete large-scale studies to date, but is an interesting topic for future research.

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Seed defense is not the only possible explanation for the lower levels of seed predation plants experience in tropical latitudes. For example, tropical communities produce more total mass of seed annually than do plant communities at higher latitudes (Moles et al. 2009). High levels of seed production have been shown to satiate seed predators and decrease levels of seed predation (Janzen 1971). Thus, tropical plants might experience less seed predation because of the high level of seed production at the community level (Moles & Westoby 2003).

However, this mechanism would not result in lower seed predation rates if the higher seed production of tropical vegetation was counterbalanced by higher seed predator populations.

At present, we do not have the necessary data to test this idea.

Empirical data tend to suggest that the often-quoted figures of pre-dispersal seed predation in

Crawley (1992) need to be downward-revised, as zero predation has been heavily under- represented in the literature. In this study, pre-dispersal seed predation (average 9%) was much lower than the speculative estimated average of 45% by Crawley (1992). However, our data from Australia are consistent with data from a global-scale literature review suggesting that pre-dispersal seed predation is usually low and only few species suffer from high levels of damage (Kolb et al. 2007). Our data for post-dispersal seed removal through 24 hours in

Australia (average 45%) is higher than the average from global data that have been scaled to reflect an exposure duration of 24 hours (average 26% across 280 species; Moles et al. 2003).

In summary, predation on naturally occurring seeds is not more intense towards the tropics.

This finding, was based on a consistent protocol across a broad range of latitudes and taxa, and is counter to the traditional predictions for a negative latitudinal gradient in the interactions between seeds and their predators. Our findings add empirical evidence to a growing body of documentation that casts doubt on our current understanding of the factors shaping global patterns in biodiversity.

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ACKNOWLEDGEMENTS

Many thanks go to Casey Gibson, Charlotte Mills, Jie Yan, Linda Wong and Yi Deng for

field work assistance, to Floret Meredith, Rhiannon Dalrymple and Stephanie Creer for beneficial discussion and logistic suggestions, and to Steritech Pty Ltd for the courtesy of gamma irradiation. We thank park administrators, especially Kerry Walsh, Micah Visoiu,

Alex Tessieri, Russell Best, Geoff James, Rachel Kempers, Keith Williams, George

Malolakis, Renee Hutchison, Richard Dakin, Bill Lennox and Andrew Hedges, for the

valuable information and guidance. S.-C. Chen was supported by Student Research Grants

from Ecological Society of Australia and School of Biological, Earth and Environmental

Sciences, and an UIPA scholarship from UNSW. A. T. Moles was supported by a QEII

fellowship from the Australian Research Council (DP0984222). Herbarium specimens were

deposited at the UNSW John T.Waterhouse herbarium.

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Drawing by Si-Chong Chen.

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Latitudinal gradient in seed predation

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SUPPORTING INFORMATION

Appendix S1 Geographic information for the study sites.

Appendix S2 Pre-dispersal seed predation and post-dispersal seed removal data.

Appendix S3 Data on the removal of standard seeds.

Appendix S4 Diagram of the 2 × 2 contingency table for the calculation of odds ratio.

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Appendix S1 Geographic information for the study sites. QLD = Queensland, NSW = New

South Wales, VIC = Victoria, TAS = Tasmania.

date of seed latitude longitude elevation site name state collection (°S) (°E) (m) (yy-mm-dd) 15-03-26 15.5025 145.2575 36 Mount Cook National Park QLD 15-03-28 16.6782 145.5707 16 Macalister Range National Park QLD 15-03-31 17.1704 145.8313 95 Wooroonooran National Park QLD 15-04-03 18.4117 145.9499 65 Girringun National Park QLD 15-04-05 19.4391 146.9470 92 Bowling Green Bay National Park QLD 15-03-23 20.2850 148.7704 47 Conway National Park QLD 15-03-21 21.6300 149.4321 13 Cape Palmerston National Park QLD 15-03-19 23.3281 150.5528 108 Mount Archer National Park QLD 15-03-17 24.2101 151.7997 12 Eurimbula National Park QLD 15-03-15 25.6167 152.8444 16 Poona National Park QLD 15-01-10 27.0165 152.6989 202 D'Aguilar National Park QLD 15-01-12 29.3395 153.2610 58 Bundjalung National Park NSW 15-01-14 30.3715 152.8656 115 Dorrigo National Park NSW 15-01-16 31.6469 152.7739 347 Dooragan National Park NSW 15-01-18 32.4123 152.4655 153 Myall Lakes National Park NSW 15-03-08 33.6634 151.2312 184 Ku-Ring-Gai Chase National Park NSW 14-12-13 34.0836 151.0554 34 Royal National Park NSW 15-02-16 34.7696 150.3831 308 Morton National Park NSW 15-02-14 36.0951 150.1197 43 Eurobodalla National Park NSW 15-02-12 37.2803 149.9217 125 Nadgee Nature Reserve NSW 15-02-10 39.0209 146.3289 34 Wilsons Promontory National Park VIC 15-02-01 40.8931 148.1613 61 Mount William National Park TAS 15-02-03 42.1343 148.3043 31 Freycinet National Park TAS 15-02-07 43.0575 147.0992 140 Snug Tiers Nature Recreation Area TAS 15-02-05 43.5829 146.8954 1 Cockle Creek, Southwest National TAS Park

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Appendix S2 Pre-dispersal seed predation and post-dispersal seed removal data. NA stands for data not available.

site name species no. species name pre-dispersal post-dispersal seed predation seed removal Mount Cook National Park sp.1 Themeda triandra 0.01 0.41 Mount Cook National Park sp.2 Vachellia farnesiana 0 0 Mount Cook National Park sp.3 Cyclophyllum maritimum 0 0.17 Mount Cook National Park sp.4 Breynia cernua 0.45 NA Mount Cook National Park sp.5 Antidesma ghaesembilla 0.48 NA Mount Cook National Park sp.6 Maytenus disperma 0.02 0.84 Mount Cook National Park sp.7 Tetrastigma thorsborneorum 0 0.45 Mount Cook National Park sp.8 Exocarpos latifolius 0 NA Mount Cook National Park sp.9 Tabernaemontana pandacaqui 0 NA Mount Cook National Park sp.10 Eucalyptus brassiana 0 NA Mount Cook National Park sp.11 Dendrocnide moroides 0 NA Mount Cook National Park sp.12 Tacca leontopetaloides 0 NA Mount Cook National Park sp.13 Sida cordifolia 0 NA Macalister Range National Park sp.1 Themeda triandra 0 0.74 Macalister Range National Park sp.2 Sorghum plumosum 0 0.37 Macalister Range National Park sp.3 Heteropogon triticeus 0 0.35 Macalister Range National Park sp.4 Tetrastigma thorsborneorum 0 0.20 Macalister Range National Park sp.5 Cassytha filiformis 0 0.17 Macalister Range National Park sp.7 Chamaecrista mimosoides 0.03 NA Macalister Range National Park sp.8 Macalister 8 Melaleuca 0 NA Wooroonooran National Park sp.1 Allocasuarina littoralis 0 0.78 Wooroonooran National Park sp.2 Pittosporum venulosum 0 0.84 Wooroonooran National Park sp.3 Hibbertia scandens 0 1.00 Wooroonooran National Park sp.4 Smilax australis 0 0.50

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Wooroonooran National Park sp.5 Pittosporum revolutum 0 NA Wooroonooran National Park sp.6 Ficus benjamina 0 NA Girringun National Park sp.1 Smilax australis 0 0.47 Girringun National Park sp.2 Eustrephus latifolius 0 0.94 Girringun National Park sp.3 Polyscias australiana 0 0.96 Girringun National Park sp.4 Cyclophyllum multiflorum 0.05 0.53 Girringun National Park sp.5 Alpinia caerulea 0.02 0.49 Girringun National Park sp.6 Geitonoplesium cymosum 0 0.62 Girringun National Park sp.7 Sorghum nitidum 0 NA Girringun National Park sp.8 Mallotus philippensis 0.45 NA Bowling Green Bay National Park sp.1 Celastraceae sp. 0 0 Bowling Green Bay National Park sp.2 Mallotus philippensis 0.65 NA Bowling Green Bay National Park sp.3 Trema tomentosa 0 0.93 Bowling Green Bay National Park sp.4 Cycas sp. 0 0 Bowling Green Bay National Park sp.5 Sorghum plumosum 0 0.54 Bowling Green Bay National Park sp.6 Smilax australis 0 NA Bowling Green Bay National Park sp.7 Callistemon viminalis 0 NA Conway National Park sp.1 Gahnia aspera 0 0.02 Conway National Park sp.2 Cassytha filiformis 0.01 0.39 Conway National Park sp.3 Dodonaea lanceolata 0.22 0.91 Conway National Park sp.4 Trema tomentosa 0 0.87 Conway National Park sp.5 Desmodium tortuosum 0 NA Conway National Park sp.6 Alyxia ruscifolia 0 0 Conway National Park sp.7 Geitonoplesium cymosum 0 0.12 Conway National Park sp.8 Elaeodendron melanocarpum 0.22 0 Conway National Park sp.9 Acacia simsii 0.20 NA Conway National Park sp.10 Tabernaemontana pandacaqui 0 NA Conway National Park sp.11 Cordyline murchisoniae 0.12 NA Conway National Park sp.12 Dianella caerulea 0 NA

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Cape Palmerston National Park sp.1 Casuarina equisetifolia 0 0.34 Cape Palmerston National Park sp.3 Cassytha filiformis 0 0.52 Cape Palmerston National Park sp.4 Polyalthia nitidissima 0 0.27 Cape Palmerston National Park sp.5 Sophora tomentosa 0.64 NA Cape Palmerston National Park sp.6 Alyxia spicata 0 0.05 Cape Palmerston National Park sp.7 Exocarpos latifolius 0.10 NA Cape Palmerston National Park sp.8 Melaleuca viridiflora 0 NA Cape Palmerston National Park sp.9 Themeda triandra 0 NA Mount Archer National Park sp.1 Celastraceae sp. 0 0.03 Mount Archer National Park sp.2 Melia azedarach 0 0.04 Mount Archer National Park sp.3 Cycas sp. 0 0.04 Mount Archer National Park sp.4 Desmanthus pernambucanus 0 0.35 Mount Archer National Park sp.5 Fabaceae sp. 0 0.44 Mount Archer National Park sp.6 Passiflora foetida 0 0.86 Mount Archer National Park sp.7 Casuarina cunninghamiana 0 NA Mount Archer National Park sp.8 Crotalaria pallida 0.04 NA Mount Archer National Park sp.9 Archer 9 0 NA Mount Archer National Park sp.10 Melaleuca dealbata 0.007 NA Eurimbula National Park sp.1 Allocasuarina littoralis 0.005 0.86 Eurimbula National Park sp.2 Allocasuarina torulosa 0 0.75 Eurimbula National Park sp.3 Eurimbula 3 0.25 0.75 Eurimbula National Park sp.4 Cassytha filiformis 0.01 0.34 Eurimbula National Park sp.5 Germainia capitata 0 0.08 Eurimbula National Park sp.6 Brachychiton bidwillii 0 0.45 Eurimbula National Park sp.7 Leptospermum polygalifolium 0.08 NA Eurimbula National Park sp.8 Themeda triandra 0.01 NA Eurimbula National Park sp.9 Lepidosperma laterale 0.55 NA Eurimbula National Park sp.10 Dodonaea lanceolata 0 NA Eurimbula National Park sp.11 Melaleuca viridiflora 0 NA

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Latitudinal gradient in seed predation

Poona National Park sp.1 Persoonia virgata 0.02 0.26 Poona National Park sp.2 Hakea actites 0.35 0.19 Poona National Park sp.3 Themeda triandra 0.09 NA Poona National Park sp.4 Cyperus sphaeroideus 0 0.48 Poona National Park sp.5 Eriachne rara 0 0.67 Poona National Park sp.6 Agiortia pedicellata 0.03 0.34 Poona National Park sp.7 Leptospermum polygalifolium 0.04 NA Poona National Park sp.8 Leptospermum trinervium 0.09 NA Poona National Park sp.9 Petrophile shirleyae 0 NA Poona National Park sp.10 Eucalyptus latisinensis 0.14 NA Poona National Park sp.11 Melaleuca quinquenervia 0 NA Poona National Park sp.12 Melaleuca thymifolia 0.04 NA D'Aguilar National Park (north section) sp.1 Cordyline petiolaris 0.07 0.46 D'Aguilar National Park (north section) sp.2 Scolopia braunii 0.70 0.16 D'Aguilar National Park (north section) sp.3 Micromelum minutum 0.05 NA D'Aguilar National Park (north section) sp.4 Geitonoplesium cymosum 0 NA D'Aguilar National Park (north section) sp.5 Lepidosperma laterale 0.50 NA D'Aguilar National Park (north section) sp.6 Abutilon oxycarpum 0.39 NA D'Aguilar National Park (north section) sp.7 Alyxia ruscifolia 0 NA D'Aguilar National Park (south section) sp.1 Themeda triandra 0.19 0.49 D'Aguilar National Park (south section) sp.2 Alpinia caerulea 0.01 NA Bundjalung National Park sp.1 Lepidosperma laterale 0.35 0.79 Bundjalung National Park sp.2 Cyanthillium cinereum 0 0.19 Bundjalung National Park sp.3 Panicum effusum 0 NA Bundjalung National Park sp.4 Leucopogon leptospermoides 0 1.00 Bundjalung National Park sp.5 Leptospermum polygalifolium 0.05 NA Bundjalung National Park sp.6 Smilax australis 0.02 NA Bundjalung National Park sp.7 Alpinia caerulea 0 NA Bundjalung National Park sp.8 Elaeocarpus reticulatus 0.03 NA

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Latitudinal gradient in seed predation

Dorrigo National Park sp.1 Morinda jasminoides 0.08 0.87 Dorrigo National Park sp.2 Syzygium luehmannii 0 1.00 Dorrigo National Park sp.3 Alpinia caerulea 0.29 0.93 Dorrigo National Park sp.4 Cryptocarya rigida 0.22 0.57 Dorrigo National Park sp.5 Linospadix monostachyos 0 0.02 Dorrigo National Park sp.6 Cordyline stricta 0.08 NA Dorrigo National Park sp.7 Dioscorea transversa 0.08 NA Dorrigo National Park sp.8 Gymnostachys anceps 0 NA Dooragan National Park sp.1 Podolobium ilicifolium 0.13 0.97 Dooragan National Park sp.2 Allocasuarina littoralis 0.07 0.92 Dooragan National Park sp.3 Hardenbergia violacea 0.11 0.83 Dooragan National Park sp.4 Themeda triandra 0.38 0.91 Dooragan National Park sp.5 Gahnia aspera 0.19 0.08 Dooragan National Park sp.6 Gahnia melanocarpa 0 0.89 Dooragan National Park sp.7 Xanthorrhoea macronema 0.66 NA Dooragan National Park sp.8 Leptospermum polygalifolium 0.07 NA Dooragan National Park sp.10 Geitonoplesium cymosum 0 NA Dooragan National Park sp.11 Dianella caerulea 0 NA Myall Lakes National Park sp.1 Dodonaea triquetra 0.85 0.96 Myall Lakes National Park sp.2 Polyscias sambucifolia 0.02 NA Myall Lakes National Park sp.3 Smilax glyciphylla 0.09 0.21 Myall Lakes National Park sp.4 Eustrephus latifolius 0.16 0.42 Myall Lakes National Park sp.5 Gahnia sieberiana 0.04 0.89 Myall Lakes National Park sp.6 Hypolaena fastigiata 0.04 0.28 Myall Lakes National Park sp.7 Kennedia rubicunda 0.25 0.74 Myall Lakes National Park sp.8 Geitonoplesium cymosum 0 NA Myall Lakes National Park sp.9 Leptospermum polygalifolium 0.05 NA Myall Lakes National Park sp.10 Trachymene incisa 0 NA Ku-Ring-Gai Chase National Park sp.1 Allocasuarina littoralis 0.02 0.85

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Latitudinal gradient in seed predation

Ku-Ring-Gai Chase National Park sp.2 Petrophile pulchella 0.08 0.35 Ku-Ring-Gai Chase National Park sp.3 Hakea teretifolia 0.10 0.40 Ku-Ring-Gai Chase National Park sp.4 Hakea gibbosa 0.04 0.39 Ku-Ring-Gai Chase National Park sp.5 Chordifex dimorphus 0.07 0.57 Ku-Ring-Gai Chase National Park sp.6 Hakea propinqua 0 NA Ku-Ring-Gai Chase National Park sp.7 Lambertia formosa 0.06 NA Ku-Ring-Gai Chase National Park sp.8 Leptospermum squarrosum 0.04 NA Ku-Ring-Gai Chase National Park sp.9 Leptospermum arachnoides 0.09 NA Ku-Ring-Gai Chase National Park sp.10 Eucalyptus haemastoma 0.08 NA Ku-Ring-Gai Chase National Park sp.11 Isopogon anethifolius 0 NA Ku-Ring-Gai Chase National Park sp.12 Pultenaea stipularis 0.40 NA Royal National Park sp.1 Leptospermum polygalifolium 0.11 NA Royal National Park sp.2 Leptospermum squarrosum 0.01 NA Royal National Park sp.3 Acacia linifolia 0.17 NA Royal National Park sp.4 Cassytha pubescens 0.02 NA Royal National Park sp.5 Lomandra longifolia 0.04 NA Royal National Park sp.6 Allocasuarina distyla 0.01 NA Royal National Park sp.7 Petrophile pulchella 0.01 NA Royal National Park sp.8 Royal 8 Cyperaceae 0.01 NA Royal National Park sp.9 Dillwynia retorta 0.18 NA Royal National Park sp.10 Hakea teretifolia 0 NA Royal National Park sp.11 Patersonia glabrata 0.01 NA Royal National Park sp.12 Isopogon anemonifolius 0 NA Morton National Park sp.1 Petrophile pulchella 0.02 0.24 Morton National Park sp.2 Gompholobium grandiflorum 0.61 0.93 Morton National Park sp.3 Haemodorum planifolium 0.16 0.06 Morton National Park sp.4 Hakea sericea 0.03 0.13 Morton National Park sp.5 Lambertia formosa 0.05 0.18 Morton National Park sp.6 Isopogon anemonifolius 0.01 NA

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Latitudinal gradient in seed predation

Morton National Park sp.7 Leptospermum polygalifolium 0.08 NA Morton National Park sp.8 Austrostipa pubescens 0.05 NA Eurobodalla National Park sp.1 Pittosporum undulatum 0 0.49 Eurobodalla National Park sp.2 Pittosporum revolutum 0 0.38 Eurobodalla National Park sp.3 Allocasuarina littoralis 0.001 0.43 Eurobodalla National Park sp.4 Acacia irrorata 0.28 0.63 Eurobodalla National Park sp.5 Elaeocarpus reticulatus 0.03 0.08 Eurobodalla National Park sp.6 Austrostipa rudis 0.03 0.30 Eurobodalla National Park sp.7 Gahnia aspera 0.10 0.69 Eurobodalla National Park sp.8 Melaleuca hypericifolia 0.19 NA Eurobodalla National Park sp.9 Gahnia grandis 0 NA Eurobodalla National Park sp.10 Lomandra longifolia 0.12 NA Eurobodalla National Park sp.11 Leptocarpus tenax 0.02 NA Eurobodalla National Park sp.12 Acaena novae-zelandiae 0 NA Nadgee Nature Reserve sp.1 Allocasuarina littoralis 0.008 0.49 Nadgee Nature Reserve sp.2 Hakea decurrens 0 0.05 Nadgee Nature Reserve sp.3 Gahnia grandis 0 0.82 Nadgee Nature Reserve sp.4 Gahnia melanocarpa 0 0.86 Nadgee Nature Reserve sp.5 Cassinia longifolia 0 NA Nadgee Nature Reserve sp.6 Austrostipa rudis 0 0.27 Nadgee Nature Reserve sp.7 Bursaria spinosa 0.86 NA Nadgee Nature Reserve sp.8 Leptospermum scoparium 0.05 NA Nadgee Nature Reserve sp.9 Melaleuca squarrosa 0.02 NA Nadgee Nature Reserve sp.10 Melaleuca armillaris 0.34 NA Wilsons Promontory National Park sp.1 Xanthorrhoea australis 0.41 0.17 Wilsons Promontory National Park sp.2 Lepidosperma squamatum 0.54 0.70 Wilsons Promontory National Park sp.3 Acacia suaveolens 0.07 0.31 Wilsons Promontory National Park sp.4 Acacia verticillata 0.05 0.34 Wilsons Promontory National Park sp.5 Coprosma quadrifida 0 0.41

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Wilsons Promontory National Park sp.6 Hakea decurrens 0 NA Wilsons Promontory National Park sp.7 Leptospermum continentale 0.04 NA Wilsons Promontory National Park sp.8 Melaleuca squarrosa 0.03 NA Wilsons Promontory National Park sp.9 Kunzea ambigua 0.04 NA Wilsons Promontory National Park sp.10 Wprom 10 Eucalyptus 0 NA Wilsons Promontory National Park sp.11 Eucalyptus baxteri 0.01 NA Wilsons Promontory National Park sp.12 Cassytha melantha 0 NA Wilsons Promontory National Park sp.13 Acacia melanoxylon 0.39 NA Mount William National Park sp.1 Hakea teretifolia 0.08 0.06 Mount William National Park sp.2 Hakea nodosa 0.13 0.71 Mount William National Park sp.3 Allocasuarina verticillata 0.01 0.35 Mount William National Park sp.4 Allocasuarina paludosa 0.005 0.31 Mount William National Park sp.5 Lomandra longifolia 0.18 1.00 Mount William National Park sp.6 Gahnia grandis 0 0.27 Mount William National Park sp.7 Lepidosperma laterale 0.38 NA Mount William National Park sp.8 Leptospermum scoparium 0.07 NA Mount William National Park sp.9 Acacia melanoxylon 0.13 NA Mount William National Park sp.10 Kunzea ambigua 0.09 NA Mount William National Park sp.11 Acaena novae-zelandiae 0 NA Mount William National Park sp.12 Eucalyptus amygdalina 0.03 NA Mount William National Park sp.13 Leucopogon lanceolatus 0 NA Freycinet National Park sp.1 Acacia stricta 0.01 0.11 Freycinet National Park sp.2 Hakea megadenia 0.30 0.11 Freycinet National Park sp.3 Allocasuarina littoralis 0.001 0.57 Freycinet National Park sp.4 Lepidosperma laterale 0.26 0.39 Freycinet National Park sp.5 Leptospermum scoparium 0.17 NA Freycinet National Park sp.6 Leptospermum glaucescens 0 NA Freycinet National Park sp.7 Leptospermum lanigerum 0.25 NA Freycinet National Park sp.8 Melaleuca gibbosa 0 NA

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Freycinet National Park sp.9 Hakea teretifolia 0 NA Freycinet National Park sp.10 Eucalyptus amygdalina 0.18 NA Freycinet National Park sp.11 Kunzea ambigua 0.17 NA Snug Tiers Nature Recreation Area sp.1 Allocasuarina zephyrea 0.01 0.47 Snug Tiers Nature Recreation Area sp.2 Themeda triandra 0 0.42 Snug Tiers Nature Recreation Area sp.3 Lomandra longifolia 0.01 0.75 Snug Tiers Nature Recreation Area sp.4 Bedfordia salicina 0 0.07 Snug Tiers Nature Recreation Area sp.5 Leptecophylla juniperina 0.06 0 Snug Tiers Nature Recreation Area sp.6 Lepidosperma laterale 0.79 0.66 Snug Tiers Nature Recreation Area sp.7 Exocarpos cupressiformis 0.04 0.09 Snug Tiers Nature Recreation Area sp.8 Callistemon pallidus 0.14 NA Snug Tiers Nature Recreation Area sp.10 Leptospermum scoparium 0.12 NA Snug Tiers Nature Recreation Area sp.11 Leptorhynchos squamatus 0 NA Snug Tiers Nature Recreation Area sp.12 Austrostipa aphylla 0 NA Snug Tiers Nature Recreation Area sp.13 Austrostipa pubinodis 0 NA Cockle Creek sp.1 Coprosma quadrifida 0 0.07 Cockle Creek sp.2 Leptecophylla juniperina 0.42 0.04 Cockle Creek sp.3 Leucopogon parviflorus 0 0.63 Cockle Creek sp.4 Pittosporum bicolor 0 0.14 Cockle Creek sp.5 Pimelea drupacea 0.03 0.08 Cockle Creek sp.6 Tasmannia lanceolata 0 NA Cockle Creek sp.7 Gahnia grandis 0 0.04 Cockle Creek sp.8 Carex appressa 0.05 NA Cockle Creek sp.9 Acacia longifolia 0.07 0 Cockle Creek sp.10 Melaleuca squarrosa 0 NA Cockle Creek sp.11 Leptospermum scoparium 0.08 NA Cockle Creek sp.12 Leptospermum lanigerum 0.06 NA Cockle Creek sp.13 Leptecophylla juniperina (white) 0.79 NA Cockle Creek sp.14 Cockle 14 Juncus 0.03 NA

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Appendix S3 Data on the removal of standard seeds.

site name removal of rice grains Mount Cook National Park 0.40 Macalister Range National Park 0.99 Wooroonooran National Park 0.99 Girringun National Park 0.99 Bowling Green Bay National Park 0.91 Conway National Park 0.41 Cape Palmerston National Park 0.93 Mount Archer National Park 0.86 Eurimbula National Park 0.96 Poona National Park 0.80 D'Aguilar National Park 0.68 Bundjalung National Park 0.98 Dorrigo National Park 1.00 Dooragan National Park 0.98 Myall Lakes National Park 0.98 Ku-Ring-Gai Chase National Park 0.95 Morton National Park 0.83 Eurobodalla National Park 0.96 Nadgee Nature Reserve 0.91 Wilsons Promontory National Park 0.80 Mount William National Park 0.11 Freycinet National Park 0.42 Snug Tiers Nature Recreation Area 0.09 Cockle Creek 0.71

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Appendix S4 Diagram of the 2 × 2 contingency table for the calculation of odds ratio.

low latitude high latitude (control) (treatment)

events number of predated seeds number of predated seeds

non-events number of survived seeds number of survived seeds

For the case of zero predation/survival, 0.5 was added to the four values for approximation

(Haddock et al. 1998).

An odds ratio > 1 [ln(OR) > 0] indicates that the odds of seed predation is higher at high latitude; an odds ratio < 1 [ln(OR) < 0] indicates that the odds of seed predation is higher at low latitude.

REFERENCE

Haddock, C.K., Rindskopf, D. & Shadish, W.R. (1998). Using odds ratios as effect sizes for

meta-analysis of dichotomous data: A primer on methods and issues. Psychological

Methods, 3, 339-353.

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Appendix S5 The effect of latitudinal difference (Δlatitude) between the northernmost site and the southernmost site for each species on the effect size (log odds ratio) of seed predation at the within-species level. Each point stands for a species. No significant effects of latitudinal difference were detected using weighted least squares regression. (a) pre-dispersal seed predation, p = 0.151; (b) post-dispersal seed removal, p = 0.927.

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Chapter four

Could physical investment in seed structures be obscuring the

latitudinal gradient in seed predation?

Formatted for submission to Journal of Ecology

Si-Chong Chen1 & Angela T. Moles1

1 – Evolution & Ecology Research Centre, School of Biological, Earth and Environmental

Sciences, University of New South Wales, Sydney, NSW 2052, Australia

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Summary

1. There has been heated debate on the existence and generality of the latitudinal

gradient in biotic interactions. Empirical studies and data syntheses have shown that

seed predation is not more intense towards the tropics. One possible explanation for

this finding is that seeds may be better defended at low latitudes than at high latitudes.

2. Our goal was to provide the first broad-scale, quantitative analysis of the latitudinal

gradient in investment in tissues surrounding seed reserve. We measured the biomass

ratio of protective tissue to seed reserve for 250 species-site combinations ranging

from 15°30'S to 43°35'S along the east coast of Australia.

3. Contrary to expectations, we found that the biomass ratio of seed structures was

greater towards high latitudes in seeds exposed to pre-dispersal seed predators, while

seeds exposed to post-dispersal seed predators did not show a latitudinal gradient in

the biomass ratio of seed structures. The physical investment in seed structures was

not correlated with the level of seed predation, at either the pre-dispersal or the post-

dispersal stage. Fleshy-fruited species invested proportionally less biomass in

structures surrounding seed reserve while suffering less pre-dispersal seed predation.

4. Synthesis. This study adds to the growing evidences that challenging the idea of a

declined latitudinal gradient in biotic interactions, and therefore cast doubt on our

current understanding of the factors driving the latitudinal gradient in plant diversity.

Our findings also demonstrate a defensive role of fleshy fruits in deterring pre-

dispersal seed predators.

Keywords: seed coat, seed defence, seed predation, granivory, herbivory, seed removal, biotic interactions

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INTRODUCTION

It has long been thought that there is a latitudinal gradient in biotic interactions, with higher levels of herbivory and predation in the tropics (Wallace 1878; Coley & Aide 1991;

Schemske et al. 2009). The increasing intensity of biotic interactions with decreasing latitude has often been used to explain the striking latitudinal gradient in biodiversity (Schemske

2002; Johnson & Rasmann 2011). However, a growing body of studies have found results contrary to the traditional theory, triggering heated debate on the generality of the latitudinal gradient in biotic interactions (HilleRisLambers, Clark & Beckage 2002; Ollerton & Cranmer

2002; Moles & Westoby 2003; Moles et al. 2011a; Poore et al. 2012; Schleuning et al. 2012;

Kozlov et al. 2015; Moles & Ollerton 2016). Intraspecific studies (García et al. 2000; Toju &

Sota 2006; Anstett, Naujokaitis-Lewis & Johnson 2014; Sanz & Pulido 2014), large-scale data syntheses (Moles & Westoby 2003; Hulme & Kollmann 2005; Peco, Laffan & Moles

2014), and a large-scale empirical study (Chapter three of this thesis), have all failed to support the hypothesis that seed predation of natural seeds in their natural habitats is more intense towards the equator. In this study, we aim to determine whether a latitudinal gradient in physical investment in seed structures might be obscuring the latitudinal gradient in seed predation, as well as comparing seed physical investment between fleshy-fruited species and dry-fruited species.

Seeds are the most vulnerable stage in plant life history (Hanley et al. 2007) and the most heavily defended plant organ (Zangerl & Bazzaz 1992). Greater investment in seed protective structures is thought to reduce the ability of seed predators and fungal pathogens to access the seed reserve (Janzen 1969; Crawley 1992; Dalling et al. 2011). One possibility is that higher levels of seed predation in the tropics in the past could have led tropical plants to evolve greater defences against seed predators, reducing seed predation levels in the tropics to a

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similar or even lower level as experienced at higher latitudes (Chapter three of this thesis;

also see Anstett et al. 2015). Latitudinal variation in seed defence has received remarkably

little attention. The only study on the latitudinal gradient in seed physical defence found that

the pericarp of Japanese camellia (Camellia japonica) was thicker towards lower latitudes

across 6° of temperate latitude (Toju et al. 2011). We aim to test at the cross-species level

whether low-latitude seeds are armed with proportionally heavier tissues surrounding their

seed reserves. Pre-dispersal and post-dispersal seed predations differ substantially in the

identity and abundance of seed predators (Hulme 1998; Kolb, Ehrlén & Eriksson 2007), and

exhibit different trends across latitudes (Chapter three of this thesis). Consequently, the type

and mass of seed defences might be different between the pre-dispersal and the post-dispersal

stages. We therefore quantify the latitudinal gradients in seed defence for both the pre-

dispersal and the post-dispersal stages.

If high levels of predation select for higher levels of defence (Stamp 2003), we may see a

positive correlation between seed defence and predation. However, a study across 65

Australian species found no relationship between post-dispersal seed removal and seed physical defence (Moles, Warton & Westoby 2003). In addition, the pericarp of Camellia japonica was thicker towards lower latitudes regardless of the presence/absence of seed predators (Toju et al. 2011), suggesting that seed defensive traits may be driven by selective forces other than seed predation. In this study, we provide a direct test of the idea that seeds that experience high levels of seed predation invest proportionally more biomass in tissues surrounding seed reserve.

Previous work has shown that species at lower latitudes experience lower levels of seed predation (Chapter three of this thesis). One possibility is that these low rates of seed

predation are related to the high proportion of fleshy-fruited species at low latitudes (Chapter

190

Latitudinal gradient in seed defence two of this thesis). Mack (2000) suggested that fleshy pericarps might provide a cheap and effective defence against pre-dispersal seed predators. Despite intuitive awareness of fleshy pulp as a seed defence with relatively low investment in dry mass (Bolmgren & Eriksson

2005), we lack quantitative evidence that fleshy fruits represent lower investment in building materials than do dry fruits. We therefore finished by asking whether species with fleshy fruits invest a smaller proportion of biomass in tissues surrounding their seed reserves, and experience lower levels of pre-dispersal seed predation than do species with dry fruits.

In summary, we test the following hypotheses:

(1) Plants invest a greater proportion of dry mass in pre- and post-dispersal tissues

surrounding seed reserve towards lower latitudes.

(2) Plants that experience higher levels of seed predation invest a greater proportion of

dry mass in tissues surrounding seed reserve.

(3) Plants bearing fleshy fruits invest a lower proportion of dry mass in pre-dispersal

tissues surrounding seed reserve and lose a lower proportion of their seeds to pre-

dispersal seed predation, than do plants bearing dry fruits.

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Latitudinal gradient in seed defence

Fig. 1. Fruits and seeds were collected in the sclerophyll forests across 25 sites from 15°30'S to 43°35'S on the east coast of Australia. From north to south, these sites are located in: (1)

Mount Cook National Park; (2) Macalister Range National Park; (3) Wooroonooran National

Park; (4) Girringun National Park; (5) Bowling Green Bay National Park; (6) Conway

National Park; (7) Cape Palmerston National Park; (8) Mount Archer National Park; (9)

Eurimbula National Park; (10) Poona National Park; (11) D'Aguilar National Park; (12)

Bundjalung National Park; (13) Dorrigo National Park; (14) Dooragan National Park; (15)

Myall Lakes National Park; (16) Ku-Ring-Gai Chase National Park; (17) Royal National Park;

(18) Morton National Park; (19) Eurobodalla National Park; (20) Nadgee Nature Reserve; (21)

Wilsons Promontory National Park; (22) Mount William National Park; (23) Freycinet

National Park; (24) Snug Tiers Nature Recreation Area; (25) Cockle Creek section in

Southwest National Park. See Appendix S1 for a detailed description of field sites.

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Latitudinal gradient in seed defence

MATERIALS AND METHODS

Site location and species selection

We sampled 169 species along a latitudinal transect of 25 sites (Fig. 1), using consistent sampling methods (Chapter three of this thesis). These sites were in protected low-altitude sclerophyll forests along the east coast of Australia, spanning 28° of latitude from Far North

Queensland (15°30'S) to southernmost Tasmania (43°35'S). Detailed site information can be found in Appendix S1, as well as in an earlier study (Chapter three of this thesis).

To be included, a species had to present a combined amount of at least 50 mature fruits from at least five individuals at each site. We collected intact fruits from as many species as possible that matched this selection at the time of fieldwork. Overall, we measured 250 species-site combinations, including 169 unique species.

Determination of seed structure biomass

For each species at each site, we weighted seed structures of intact fruits (usually ten or more) on a XS105 analytical balance (Mettler Toledo GmbH, Greifensee, Switzerland), accurate to

0.1 mg. Single-seeded species comprised 91 out of 169 species-site combinations. Among these, we weighed 76% with at least ten intact fruits, 13% with five to nine intact fruits, and the remaining 11% with fewer than five intact fruits due to difficulties in processing or a lack of intact fruits. Multi-seeded fruits made up 159 species-site combinations. Of these, we

weighed 53% with at least ten intact fruits, 37% with five to nine intact fruits, and the

remaining 10% with fewer than five intact fruits. Over 90% of multi-seeded species were

weighed with at least ten intact fruits.

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Latitudinal gradient in seed defence

Fruits were separated into fruit tissues, seed coats and seed reserves, and oven dried at 60°C for three days. Seed reserve consisted of the nutritive tissues inside the seed coat, including embryo and endosperm (Leishman et al. 2000). Structures surrounding seed reserve, such as fruit tissue and seed coat, are often regarded as seed defence-related investment in both fleshy and dry fruits (e.g. Mack 2000; Prasifka, Hulke & Seiler 2014; Tiansawat et al. 2014).

Although these structures may reflect adaptations to a variety of selective forces including seed dispersal, and protection of seeds from abiotic threats such as fire (discussed in Moles et al. 2011b), one of their effects is to provide a defensive barrier that a predator has to penetrate to access seed reserve. Following Moles, Warton and Westoby (2003), “post-

dispersal seed protection” was calculated as the mass of post-dispersal investment divided by seed reserve mass. Post-dispersal investment consisted of seed coat (also known as testa) that provided barriers against post-dispersal seed predators when the seed was dispersed away from its mother plant. “Pre-dispersal seed protection” was calculated as the mass of pre- dispersal investment divided by seed reserve mass. Pre-dispersal investment consisted of fruit tissues (such as pericarps, husks and/or other floristic structures, terms depending on taxonomy), seed coats, and some appendages (e.g. fleshy receptacle of Exocarpos species, arils of Hibbertia scandens and Acacia species, bracts of spikelets of Hypolaena fastigiata, and glumes of grasses and sedges), which provided barriers against pre-dispersal seed predators when the seed was still attached to its mother plant. Structures providing no barriers against seed predation (e.g. receptacles lying below the seeds in Asteraceae and Isopogon species) were not included in defensive tissue mass. Biomass data of seed structures are presented in Appendix S1.

When seeds of some species were too small to be practically separated into seed coat and seed reserve, we set the dry weight ratio of seed coat to seed reserve to be 1, following the

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Latitudinal gradient in seed defence

summarized result of 84 Australian species in Moles, Warton and Westoby (2003). We

applied this approximation to Syzygium luehmannii, Ficus benjamina, Cassinia longifolia,

Leptorhynchos squamatus and Abutilon oxycarpum, as well as to all species in the genera of

Callistemon (2 species-site combinations), Eucalyptus (7), Kunzea (3), Leptospermum (20),

Melaleuca (12), Juncus (1). Data analyses excluding these 50 species-site combinations gave

qualitatively similar results to analyses of the full dataset. Therefore, we have presented the

results of analyses including all the data to cover the greatest statistical strength and

taxonomic breadth.

Data analysis

All mass data were log10-transformed before analysis.

The latitudinal gradient in seed physical investment was analyzed by regressing relative

biomass on latitude using linear mixed-effects model with a fixed-effect term for latitude and a random-effect term for site. Species-site combinations were taken as the replicates. The

random-effect term for site allowed the model to account for site-to-site variation in seed

physical protection that was not explained by latitude, and to quantify the proportion of

unexplained variation that lay within vs across sites. Linear mixed effects models were fitted

using restricted maximum likelihood via the lmer function in the lme4 package (Bates et al.

2014) in R version 3.0.3 (R Core Team 2014). Linearity and normality were evaluated

visually. The statistical significance of fixed effects in the linear models was evaluated using

likelihood ratio tests of the full model with latitude against the null model without latitude.

Data for pre-dispersal seed predation and post-dispersal seed removal were measured on

seeds at the same sites and from the same individual plants from which we collected seeds to

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Latitudinal gradient in seed defence

quantify pre- and post-dispersal investment (Chapter three of this thesis). The average

proportions of seed predation and seed removal were logit-transformed for each species-site

combination, to fulfill linear modeling assumptions (Warton & Hui 2011). As logit function

does not allow zero values in the numerator or denominator, we set an additive replacement

(ε) to be half of the smallest non-zero value in the date sets of pre-dispersal seed predation

and post-dispersal seed removal, respectively. We replaced all 0 values with ε and all 1

values with 1- ε, which preserved the symmetry and shape of logit function curve. The

relationships between seed predation and biomass investment in seed structures were

analysed using linear regression models in R version 3.0.3 (R Core Team 2014).

Species with fleshy vs dry fruits were defined following Chen and Moles (2015). Fleshy

fruits were represented by 80 species-site combinations, and dry fruits by 170 species-site

combinations. We performed a two-sided t-test to compare pre-dispersal investment and

predation between fleshy and dry fruits.

RESULTS

Pre-dispersal seed protection ranged nearly 950 fold, from 0.250 g per gram of seed reserve

(Cyanthillium cinereum) to 235 g per gram of seed reserve (Hakea propinqua). Pre-dispersal

physical protection was weakly related to latitude (χ2 = 4.437, p = 0.035), but in the opposite

direction to our prediction, increasing with increasing latitude (slope = 0.013, Fig. 2a).

Overall, our model indicates that pre-dispersal protection increases from 3.523 g·g-1 at

15°30'S to 8.075 g·g-1 at 43°35'S.

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Latitudinal gradient in seed defence

Fig. 2. Pre-dispersal (a) and post-dispersal (b) seed protection (log10-transformed) across latitudes. Seed protection was calculated as biomass ratio of seed tissues surrounding seed reserve (see methods for details). Each point represents the mean value for a species at a study site; the line was fit using a linear mixed effects model (p = 0.035).

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Latitudinal gradient in seed defence

Fig. 3. (a) Pre-dispersal seed protection vs. seed predation. N = 249 species-site combinations, p = 0.08. Points aggregating near the bottom represent zero predation. (b) Post-dispersal seed protection vs. seed removal after 24 hours of exposure to post-dispersal seed predators. N =

126 species-site combinations, p = 0.33. Each data point represents a mean value for one species-site combination.

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Latitudinal gradient in seed defence

Post-dispersal seed protection ranged nearly 1,500 fold, from 0.02 g per gram of seed reserve

(Lomandra longifolia) to 22.9 g per gram of seed reserve (Gahnia aspera). There was a no

significant latitudinal gradient in post-dispersal physical protection (χ2 = 0.053, p = 0.819, Fig.

2b).

Pre-dispersal seed protection was not correlated with pre-dispersal seed predation (N = 249

species-site combinations, p = 0.08; Fig. 3a). Similarly, post-dispersal seed protection was

not correlated with post-dispersal seed removal (N = 126 species-site combinations, p = 0.33;

Fig. 3b).

Plants with fleshy fruits invested significantly less dry mass per gram of seed reserve in pre- dispersal protection than did plants with dry fruits (mean 3.712 g·g-1 vs 6.898 g·g-1, p < 0.001,

Fig. 4a). Plants with fleshy fruits also lost a lower proportion of their seeds to pre-dispersal seed predation than did plants with dry fruits (mean 0.37% vs 1.49%, p < 0.001, Fig. 4b).

DISCUSSION

We found no evidence to support the idea that physical investment in seed structures is stronger towards low latitudes. On the contrary, seeds invest proportionally greater biomass in tissues surrounding seed reserve towards the poles when they are still attached to their mother plant, and there is no latitudinal gradient in seed investment after dispersal. These results contradict the traditional ideas on this topic (Schemske et al. 2009). Together with the result that seed predation is not stronger at low latitudes (Chapter three of this thesis), our large-scale empirical studies cast serious doubt on the traditional idea of a latitudinal gradient

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in seed predation and resistance to seed predation and our previous understanding of the

factors shaping the high diversity of tropical plant species.

Fig. 4. Pre-dispersal seed protection (a) and pre-dispersal seed predation (b) between dry fruits and fleshy fruits. Data for seed predation are from Chapter three of this thesis, collected

from the same individual plants at the same study sites. The sample sizes are 170 species-site

combinations for dry fruits and 80 species-site combinations for fleshy fruits, for both

protection and predation.

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Latitudinal gradient in seed defence

The lack of a latitudinal gradient in seed investment in the present study is consistent with the

findings of recent global studies on leaf defence. A meta-analysis (Moles et al. 2011a) and an

empirical study (Moles et al. 2011b) both found that leaf defences tend to be stronger towards

high latitudes. An explanation for the positive latitudinal gradient in plant defence is that the

loss of a seed in a resource-poor environment may represent a greater cost to a plant than

would an equal amount of seed loss in a resource-rich environment (Janzen 1974). The

resource availability hypothesis predicts that slow-growing plants in resource-limited

environments (e.g. cold or arid environments) will invest more in constitutive defences than

do species from more productive habitats (e.g. tropical regions) (Coley, Bryant & Chapin

1985; Endara & Coley 2011). Bryant, Chapin and Klein (1983) has proposed that plants in

boreal habitats prone to utilise more carbon-based physical defences because of the constrained availability of nutrient resources in the environment. Our findings are consistent with this idea, by proving that plant resistance against pests tends to be higher towards the poles.

The relationships between latitude and seed investment revealed by this study are consistent with the relationships between latitude and seed predation found for the same species at the same sites (Fig. 2 and Chapter three of this thesis). That is, both pre-dispersal seed physical investment and pre-dispersal seed predation are higher at high latitudes, while neither post- dispersal seed physical investment nor post-dispersal seed removal are significantly related to latitude (Fig. 2 and Chapter three of this thesis). Despite the similar trends, we found no significant relationship between seed physical investment and seed predation, either at the pre-dispersal or post-dispersal stages (Fig. 3). This finding is not consistent with the idea that herbivores select most strongly on physical resistance traits (Carmona, Lajeunesse & Johnson

2011).

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Latitudinal gradient in seed defence

There are at least three potential explanations for the lack of relationship between seed

predation and the investment in tissues surrounding seed reserve. First, a thick layer of pericarp or seed coat may not effectively deter some guilds of seed predators. In some cases, invertebrate herbivores oviposit on developing seeds prior to the development or toughening of the defensive tissues surrounding seeds (McCall & Irwin 2006; Johnson, Campbell &

Barrett 2015). This might be the case for some of our Hakea species, which invest substantial

biomass in woody follicles (e.g. pre-dispersal protection is 142 g·g-1 for Hakea actites) but

still exhibit exit holes on a considerable proportion of fruits (e.g. pre-dispersal seed predation

is 35% for Hakea actites; see Appendix S1). Second, tissues surrounding seed reserve play an

important role in providing the primary defence not only against harmful pests but also

against unfavourable environmental conditions (Mohamed-Yasseen et al. 1994; Moles et al.

2011b). In fact, many plant resistance traits may not necessarily be good predictors of

herbivore deterrence (Carmona, Lajeunesse & Johnson 2011), but could be a part of

ramifications of plant growth history and/or the results of abiotic environments (discussed in

Moles et al. 2011b). For example, thick pericarps or seed coats (as represented by many of

our studied species) are prevalent in fire-prone environments and may be a mechanism for

protecting seeds against intense heat, or enforcing delayed germination (Hanley et al. 2007).

Third, the interplay of plant-enemy co-evolutionary dynamics requires the plants to run all

out to counterbalance the increased herbivore susceptibility. This process is a Red-Queen

style arms race in which both seed predators and plants are investing ever more in offences

and defences in order to maintain the same level of interaction (Van Valen 1973; Jokela,

Schmid-Hempel & Rigby 2000). Thus, the role of the investment in seed structures may be

more complex than is often acknowledged.

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Latitudinal gradient in seed defence

Our comparison of the difference in pre-dispersal investment in tissues surrounding seed reserve between fleshy-fruited species and dry-fruited species supports the idea that fleshy pulp is a cheap but effective defence against seed predators (Mack 2000). Fleshy-fruited

species invest proportionally less biomass in pericarp (Fig. 4a) but their seeds suffer only a

quarter of the pre-dispersal seed predation experienced by dry-fruited species (Fig. 4b). Our

findings are consistent with the idea that a high diversity of fleshy-fruited species in the tropics (as found by Chapter two of this thesis) could be a possible reason for the reduced pre-dispersal seed investment and predation seen at low latitudes.

In summary, our study provides the first comprehensive quantification of the latitudinal gradient in physical investment in seed structures. We found that the proportional investment in tissues surrounding seed reserve was not greater towards the equator. If fact, the proportional investment in pre-dispersal structures was ever higher towards the poles.

Combined with findings from a recent empirical study on seed predation (Chapter three of

this thesis), our results strongly suggest that the traditional idea of a higher levels of seed

predation and resistance to seed predation in the tropics needs to be overturned, and our

understandings of the correlation between seed investment and seed predation need to be

reinforced.

ACKNOWLEDGEMENTS

We thank Fang Chen, Frank Hemmings, Linda Wong and Alice Sun for the assistance on

data collection. S.-C. Chen wishes to thank Floret Meredith for constant beneficial discussion

and constructive comments. S.-C. Chen was supported by Student Research Grants from

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Latitudinal gradient in seed defence

Ecological Society of Australia and School of Biological, Earth and Environmental Sciences, and an UIPA scholarship from UNSW. A. T. Moles was supported by a QEII fellowship from the Australian Research Council (DP0984222). Herbarium specimens were deposited at the

UNSW John T.Waterhouse herbarium.

Drawing by Si-Chong Chen.

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Latitudinal gradient in seed defence

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Appendix S1. Geographic information for the study sites, and the data for pre-dispersal seed protection (pre-dispersal protective tissue mass per unit of seed reserve, g·g-1) and post-dispersal seed protection (post-dispersal protective tissue mass per unit of seed reserve, g·g-1). NA stands for data not available.

Record latitude longitude site name species names fruit seed pre- post- no. type mass dispersal dispersal (mg) protection protection 1 19.44 146.95 Bowling Green Bay NP Celastraceae sp. fleshy 238.00 3.38 1.55 2 19.44 146.95 Bowling Green Bay NP Mallotus philippensis dry 18.39 7.88 1.76 3 19.44 146.95 Bowling Green Bay NP Trema tomentosa fleshy 3.62 5.96 2.55 4 19.44 146.95 Bowling Green Bay NP Cycas sp. fleshy 10477.37 2.32 0.54 5 19.44 146.95 Bowling Green Bay NP Sorghum plumosum dry 6.15 2.15 1.21 6 19.44 146.95 Bowling Green Bay NP Smilax australis fleshy 43.10 1.16 0.10 7 19.44 146.95 Bowling Green Bay NP Callistemon viminalis dry 0.06 6.50 1.00 8 29.34 153.26 Bundjalung NP Lepidosperma laterale dry 3.40 4.63 1.91 9 29.34 153.26 Bundjalung NP Cyanthillium cinereum dry 0.20 0.25 0.25 10 29.34 153.26 Bundjalung NP Panicum effusum dry 0.70 0.37 0.17 11 29.36 153.31 Bundjalung NP Leucopogon leptospermoides fleshy 4.68 12.97 5.88 12 29.36 153.31 Bundjalung NP Leptospermum polygalifolium dry 0.09 5.96 1.00 13 29.36 153.31 Bundjalung NP Smilax australis fleshy 49.35 0.47 0.05 14 29.36 153.31 Bundjalung NP Elaeocarpus reticulatus fleshy 61.80 20.88 15.89 15 21.63 149.43 Cape Palmerston NP Casuarina equisetifolia dry 2.03 30.11 2.26 16 21.63 149.43 Cape Palmerston NP Cassytha filiformis fleshy 13.60 4.79 2.02 17 21.63 149.43 Cape Palmerston NP Polyalthia nitidissima fleshy 80.50 3.33 0.52 18 21.63 149.43 Cape Palmerston NP Sophora tomentosa dry 130.39 1.39 0.53 19 21.63 149.43 Cape Palmerston NP Alyxia spicata fleshy 171.64 0.48 0.21

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20 21.63 149.43 Cape Palmerston NP Exocarpos latifolius fleshy 139.42 2.09 0.81 21 21.63 149.43 Cape Palmerston NP Melaleuca viridiflora dry 0.04 5.12 1.00 22 21.63 149.43 Cape Palmerston NP Themeda triandra dry 1.98 3.21 0.60 23 43.58 146.90 Cockle Creek Coprosma quadrifida fleshy 3.22 4.12 1.19 24 43.58 146.90 Cockle Creek Leptecophylla juniperina fleshy 11.10 13.19 4.55 25 43.58 146.90 Cockle Creek Leucopogon parviflorus fleshy 3.90 10.21 6.09 26 43.58 146.90 Cockle Creek Pittosporum bicolor fleshy 8.22 1.59 0.50 27 43.58 146.90 Cockle Creek Pimelea drupacea fleshy 9.14 3.17 1.04 28 43.58 146.90 Cockle Creek Tasmannia lanceolata fleshy 3.34 2.25 2.06 29 43.58 146.90 Cockle Creek Gahnia grandis dry 6.38 22.03 17.63 30 43.58 146.90 Cockle Creek Carex appressa dry 1.10 0.67 0.57 31 43.58 146.90 Cockle Creek Acacia longifolia fleshy 37.13 3.72 0.52 32 43.58 146.90 Cockle Creek Melaleuca squarrosa dry 0.06 21.10 1.00 33 43.58 146.90 Cockle Creek Leptospermum scoparium dry 0.10 3.67 1.00 34 43.58 146.90 Cockle Creek Leptospermum lanigerum dry 0.15 5.65 1.00 35 43.58 146.90 Cockle Creek Leptecophylla juniperina (white) fleshy 16.53 14.46 10.02 36 43.58 146.90 Cockle Creek Cockle 14 Juncus dry 0.00 1.85 1.00 37 20.28 148.77 Conway NP Gahnia aspera dry 38.64 26.32 22.89 38 20.28 148.77 Conway NP Cassytha filiformis fleshy 27.91 2.75 1.51 39 20.28 148.77 Conway NP Dodonaea lanceolata dry 2.80 7.21 2.45 40 20.28 148.77 Conway NP Trema tomentosa fleshy 3.70 4.14 1.60 41 20.28 148.77 Conway NP Desmodium tortuosum dry 2.53 0.76 0.45 42 20.28 148.77 Conway NP Alyxia ruscifolia fleshy 346.93 1.72 1.10 43 20.28 148.77 Conway NP Geitonoplesium cymosum fleshy 20.20 1.33 0.63 44 20.28 148.77 Conway NP Elaeodendron melanocarpum fleshy 2122.68 24.80 21.41 45 20.28 148.77 Conway NP Acacia simsii dry 10.07 2.24 0.51 46 20.28 148.77 Conway NP Tabernaemontana pandacaqui fleshy 21.25 53.62 16.02 47 20.28 148.77 Conway NP Cordyline murchisoniae fleshy 15.26 2.62 0.69 48 20.28 148.77 Conway NP Dianella caerulea fleshy 7.65 1.60 0.73

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Latitudinal gradient in seed defence

49 27.02 152.70 D'Aguilar NP Cordyline petiolaris fleshy 3.08 3.74 3.03 50 27.02 152.70 D'Aguilar NP Scolopia braunii fleshy 11.92 7.58 1.84 51 27.02 152.70 D'Aguilar NP Micromelum minutum fleshy 24.15 1.00 0.17 52 27.02 152.70 D'Aguilar NP Geitonoplesium cymosum fleshy 13.62 1.33 0.43 53 27.02 152.70 D'Aguilar NP Lepidosperma laterale dry 2.76 5.34 2.78 54 27.02 152.70 D'Aguilar NP Abutilon oxycarpum dry 1.29 3.33 0.70 55 27.02 152.70 D'Aguilar NP Alyxia ruscifolia fleshy 376.73 0.75 0.45 56 27.41 152.88 D'Aguilar NP Themeda triandra dry 1.70 6.78 0.89 57 27.41 152.88 D'Aguilar NP Alpinia caerulea dry 2.50 4.81 1.50 58 27.41 152.88 D'Aguilar NP Melinis repens dry 0.22 5.73 1.00 59 31.65 152.77 Dooragan NP Podolobium ilicifolium dry 3.12 3.83 1.35 60 31.65 152.77 Dooragan NP Allocasuarina littoralis dry 2.27 18.47 0.88 61 31.65 152.77 Dooragan NP Hardenbergia violacea dry 15.65 4.15 1.41 62 31.65 152.77 Dooragan NP Themeda triandra dry 3.77 3.66 0.76 63 31.65 152.77 Dooragan NP Gahnia aspera dry 53.07 8.04 6.71 64 31.65 152.77 Dooragan NP Gahnia melanocarpa dry 5.86 5.15 4.52 65 31.65 152.77 Dooragan NP Xanthorrhoea macronema dry 15.66 4.05 0.89 66 31.65 152.77 Dooragan NP Leptospermum polygalifolium dry 0.13 3.99 1.00 67 31.65 152.77 Dooragan NP Geitonoplesium cymosum fleshy 14.56 1.42 0.50 68 30.37 152.87 Dorrigo NP Morinda jasminoides dry 7.34 4.98 3.59 69 30.37 152.87 Dorrigo NP Syzygium luehmannii fleshy 0.84 2.47 1.00 70 30.37 152.87 Dorrigo NP Alpinia caerulea dry 6.72 2.53 1.41 71 30.37 152.87 Dorrigo NP Cryptocarya rigida fleshy 351.63 0.43 0.11 72 30.37 152.87 Dorrigo NP Linospadix monostachyos fleshy 82.46 0.71 0.28 73 30.37 152.87 Dorrigo NP Cordyline stricta fleshy 7.23 0.80 0.34 74 30.37 152.87 Dorrigo NP Dioscorea transversa dry 4.62 8.88 1.57 75 30.37 152.87 Dorrigo NP Gymnostachys anceps fleshy 51.20 0.74 0.45 76 24.21 151.80 Eurimbula NP Allocasuarina littoralis dry 2.42 19.97 1.47 77 24.21 151.80 Eurimbula NP Allocasuarina torulosa dry 4.64 31.62 0.94

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Latitudinal gradient in seed defence

78 24.21 151.80 Eurimbula NP Eurimbula 3 fleshy 34.60 16.38 1.40 79 24.21 151.80 Eurimbula NP Cassytha filiformis fleshy 20.58 2.65 2.09 80 24.21 151.80 Eurimbula NP Germainia capitata dry 14.13 16.67 7.46 81 24.21 151.80 Eurimbula NP Brachychiton bidwillii dry 378.87 2.90 0.83 82 24.21 151.80 Eurimbula NP Leptospermum polygalifolium dry 0.16 4.77 1.00 83 24.21 151.80 Eurimbula NP Themeda triandra dry 3.40 2.85 0.62 84 24.21 151.80 Eurimbula NP Lepidosperma laterale dry 5.23 1.27 0.63 85 24.21 151.80 Eurimbula NP Dodonaea lanceolata dry 3.75 3.00 1.05 86 24.21 151.80 Eurimbula NP Melaleuca viridiflora dry 0.06 7.04 1.00 87 36.15 150.11 Eurobodalla NP Pittosporum undulatum fleshy 5.44 3.86 0.50 88 36.15 150.11 Eurobodalla NP Pittosporum revolutum fleshy 11.01 4.40 0.50 89 36.10 150.12 Eurobodalla NP Allocasuarina littoralis dry 2.19 160.85 8.60 90 36.15 150.11 Eurobodalla NP Acacia irrorata dry 11.66 4.28 0.68 91 36.15 150.11 Eurobodalla NP Elaeocarpus reticulatus fleshy 68.63 11.43 8.02 92 36.10 150.12 Eurobodalla NP Austrostipa rudis dry 3.79 1.45 1.31 93 36.10 150.12 Eurobodalla NP Gahnia aspera dry 3.00 17.50 14.00 94 36.10 150.12 Eurobodalla NP Melaleuca hypericifolia dry 0.04 11.25 1.00 95 36.09 150.13 Eurobodalla NP Gahnia grandis dry 4.79 9.61 7.69 96 36.09 150.13 Eurobodalla NP Lomandra longifolia dry 8.50 0.71 0.02 97 36.09 150.13 Eurobodalla NP Leptocarpus tenax dry 2.98 1.66 1.00 98 36.09 150.13 Eurobodalla NP Acaena novae-zelandiae dry 2.10 3.04 3.04 99 42.13 148.30 Freycinet NP Acacia stricta dry 28.89 4.85 0.86 100 42.13 148.30 Freycinet NP Hakea megadenia dry 5.55 126.24 0.26 101 42.13 148.30 Freycinet NP Allocasuarina littoralis dry 4.61 34.51 1.68 102 42.13 148.30 Freycinet NP Lepidosperma laterale dry 1.92 25.48 18.20 103 42.13 148.30 Freycinet NP Leptospermum scoparium dry 0.09 3.79 1.00 104 42.13 148.30 Freycinet NP Leptospermum glaucescens dry 0.10 21.40 1.00 105 42.13 148.30 Freycinet NP Leptospermum lanigerum dry 0.15 5.02 1.00 106 42.13 148.30 Freycinet NP Melaleuca gibbosa dry 0.03 37.93 1.00

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Latitudinal gradient in seed defence

107 42.13 148.30 Freycinet NP Hakea teretifolia dry 5.55 19.79 0.26 108 42.13 148.30 Freycinet NP Eucalyptus amygdalina dry 0.27 14.58 1.00 109 42.13 148.30 Freycinet NP Kunzea ambigua dry 0.02 11.32 1.00 110 18.41 145.95 Girringun NP Smilax australis fleshy 22.00 0.81 0.10 111 18.41 145.95 Girringun NP Eustrephus latifolius fleshy 21.60 0.72 0.06 112 18.45 146.06 Girringun NP Polyscias australiana fleshy 8.57 4.74 1.37 113 18.45 146.06 Girringun NP Cyclophyllum multiflorum fleshy 51.85 35.73 18.01 114 18.41 145.95 Girringun NP Alpinia caerulea dry 12.93 2.20 1.50 115 18.41 145.95 Girringun NP Geitonoplesium cymosum fleshy 17.10 1.02 0.60 116 18.41 145.95 Girringun NP Sorghum nitidum dry 1.77 1.19 1.00 117 18.45 146.06 Girringun NP Mallotus philippensis dry 18.39 7.88 1.76 118 33.66 151.23 Ku-Ring-Gai Chase NP Allocasuarina littoralis dry 2.77 41.93 1.98 119 33.66 151.23 Ku-Ring-Gai Chase NP Petrophile pulchella dry 6.29 34.53 4.36 120 33.66 151.23 Ku-Ring-Gai Chase NP Hakea teretifolia dry 9.50 35.37 0.23 121 33.66 151.23 Ku-Ring-Gai Chase NP Hakea gibbosa dry 57.15 125.92 0.33 122 33.66 151.23 Ku-Ring-Gai Chase NP Chordifex dimorphus dry 1.26 10.79 3.40 123 33.66 151.23 Ku-Ring-Gai Chase NP Hakea propinqua dry 58.91 235.34 0.35 124 33.66 151.23 Ku-Ring-Gai Chase NP Lambertia formosa dry 22.65 9.75 0.55 125 33.66 151.23 Ku-Ring-Gai Chase NP Leptospermum squarrosum dry 0.18 4.62 1.00 126 33.66 151.23 Ku-Ring-Gai Chase NP Leptospermum arachnoides dry 0.11 10.64 1.00 127 33.66 151.23 Ku-Ring-Gai Chase NP Eucalyptus haemastoma dry 0.43 32.80 1.00 128 33.66 151.23 Ku-Ring-Gai Chase NP Isopogon anethifolius dry 3.54 33.15 1.09 129 33.66 151.23 Ku-Ring-Gai Chase NP Pultenaea stipularis dry 10.70 6.33 1.61 130 16.68 145.57 Macalister Range NP Themeda triandra dry 4.42 2.81 0.81 131 16.68 145.57 Macalister Range NP Sorghum plumosum dry 6.60 1.63 1.06 132 16.68 145.57 Macalister Range NP Heteropogon triticeus dry 37.15 5.46 4.80 133 16.68 145.57 Macalister Range NP Tetrastigma thorsborneorum fleshy 35.64 1.79 0.92 134 16.68 145.57 Macalister Range NP Cassytha filiformis fleshy 26.05 3.25 1.71 135 16.68 145.57 Macalister Range NP Chamaecrista mimosoides dry 2.58 4.98 1.64

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Latitudinal gradient in seed defence

136 16.68 145.57 Macalister Range NP Macalister 8 Melaleuca dry 0.03 4.90 1.00 137 34.77 150.38 Morton NP Petrophile pulchella dry 4.37 26.47 1.72 138 34.77 150.38 Morton NP Gompholobium grandiflorum dry 3.75 4.04 0.69 139 34.77 150.38 Morton NP Haemodorum planifolium dry 8.87 4.61 1.03 140 34.77 150.38 Morton NP Hakea sericea dry 46.14 128.73 0.29 141 34.77 150.38 Morton NP Lambertia formosa dry 15.97 18.43 0.69 142 34.77 150.38 Morton NP Isopogon anemonifolius dry 3.70 35.17 0.72 143 34.77 150.38 Morton NP Leptospermum polygalifolium dry 0.09 3.73 1.00 144 34.77 150.38 Morton NP Austrostipa pubescens dry 22.28 4.60 4.20 145 23.33 150.55 Mount Archer NP Celastraceae sp. fleshy 164.99 3.22 0.79 146 23.33 150.55 Mount Archer NP Melia azedarach fleshy 422.73 20.54 16.75 147 23.33 150.55 Mount Archer NP Cycas sp. fleshy 12859.40 1.82 0.41 148 23.33 150.55 Mount Archer NP Desmanthus pernambucanus dry 3.96 2.17 1.30 149 23.33 150.55 Mount Archer NP Fabaceae sp. dry 6.27 1.24 0.30 150 23.33 150.55 Mount Archer NP Passiflora foetida fleshy 9.96 5.06 2.83 151 23.33 150.55 Mount Archer NP Casuarina cunninghamiana dry 0.53 14.39 1.65 152 23.33 150.55 Mount Archer NP Crotalaria pallida dry 5.47 2.37 1.19 153 23.33 150.55 Mount Archer NP Archer 9 dry 63.04 12.49 0.44 154 23.33 150.55 Mount Archer NP Melaleuca dealbata dry 0.07 5.86 1.00 155 15.50 145.26 Mount Cook NP Themeda triandra dry 3.93 5.38 1.17 156 15.50 145.26 Mount Cook NP Vachellia farnesiana dry 72.28 1.59 1.38 157 15.50 145.26 Mount Cook NP Cyclophyllum maritimum fleshy 95.52 5.49 3.58 158 15.50 145.26 Mount Cook NP Breynia cernua fleshy 3.70 7.25 3.63 159 15.50 145.26 Mount Cook NP Antidesma ghaesembilla fleshy 21.64 27.60 21.14 160 15.50 145.26 Mount Cook NP Maytenus disperma fleshy 10.41 22.65 4.51 161 15.50 145.26 Mount Cook NP Tetrastigma thorsborneorum fleshy 45.00 1.64 0.61 162 15.50 145.26 Mount Cook NP Exocarpos latifolius fleshy 172.29 1.75 0.62 163 15.50 145.26 Mount Cook NP Tabernaemontana pandacaqui fleshy 31.43 7.28 2.68 164 15.50 145.26 Mount Cook NP Eucalyptus brassiana dry 0.19 6.89 1.00

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Latitudinal gradient in seed defence

165 15.50 145.26 Mount Cook NP Dendrocnide moroides fleshy 109.42 52.82 13.52 166 15.50 145.26 Mount Cook NP Tacca leontopetaloides fleshy 15.07 1.88 0.67 167 15.50 145.26 Mount Cook NP Sida cordifolia dry 1.84 2.17 0.55 168 40.89 148.16 Mount William NP Hakea teretifolia dry 4.55 40.75 0.21 169 40.89 148.16 Mount William NP Hakea nodosa dry 10.51 156.73 0.29 170 40.89 148.16 Mount William NP Allocasuarina verticillata dry 2.18 42.40 1.84 171 40.89 148.16 Mount William NP Allocasuarina paludosa dry 0.60 60.02 4.45 172 40.89 148.16 Mount William NP Lomandra longifolia dry 5.33 0.93 0.03 173 40.89 148.16 Mount William NP Gahnia grandis dry 6.13 8.46 6.78 174 40.89 148.16 Mount William NP Lepidosperma laterale dry 3.08 2.19 1.57 175 40.89 148.16 Mount William NP Leptospermum scoparium dry 0.10 3.94 1.00 176 40.89 148.16 Mount William NP Acacia melanoxylon fleshy 15.82 5.94 1.03 177 40.89 148.16 Mount William NP Kunzea ambigua dry 0.04 18.67 1.00 178 40.89 148.16 Mount William NP Acaena novae-zelandiae dry 1.77 1.41 1.41 179 40.89 148.16 Mount William NP Eucalyptus amygdalina dry 0.24 12.89 1.00 180 40.89 148.16 Mount William NP Leucopogon lanceolatus fleshy 4.05 27.52 14.00 181 32.41 152.47 Myall Lakes NP Dodonaea triquetra dry 3.25 2.65 0.97 182 32.41 152.47 Myall Lakes NP Polyscias sambucifolia fleshy 2.69 3.79 0.93 183 32.41 152.47 Myall Lakes NP Smilax glyciphylla fleshy 53.19 0.51 0.06 184 32.41 152.47 Myall Lakes NP Eustrephus latifolius fleshy 41.97 1.27 0.14 185 32.41 152.47 Myall Lakes NP Gahnia sieberiana dry 3.86 8.90 7.04 186 32.41 152.47 Myall Lakes NP Hypolaena fastigiata dry 4.77 28.11 16.08 187 32.41 152.47 Myall Lakes NP Kennedia rubicunda dry 16.91 3.43 0.71 188 32.41 152.47 Myall Lakes NP Leptospermum polygalifolium dry 0.13 5.37 1.00 189 32.41 152.47 Myall Lakes NP Trachymene incisa dry 1.10 0.38 0.38 190 32.41 152.47 Myall Lakes NP Acacia ulicifolia dry 14.67 3.43 0.50 191 37.28 149.92 Nadgee NR Allocasuarina littoralis dry 2.58 56.89 2.65 192 37.28 149.92 Nadgee NR Hakea decurrens dry 33.07 48.05 0.25 193 37.31 149.92 Nadgee NR Gahnia grandis dry 3.48 8.64 6.91

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Latitudinal gradient in seed defence

194 37.31 149.92 Nadgee NR Gahnia melanocarpa dry 2.00 2.08 1.00 195 37.28 149.92 Nadgee NR Cassinia longifolia dry 0.07 1.00 1.00 196 37.28 149.92 Nadgee NR Austrostipa rudis dry 4.53 1.86 1.72 197 37.28 149.92 Nadgee NR Bursaria spinosa dry 0.73 9.89 1.00 198 37.31 149.92 Nadgee NR Leptospermum scoparium dry 0.14 3.62 1.00 199 37.31 149.92 Nadgee NR Melaleuca squarrosa dry 0.05 18.11 1.00 200 37.31 149.92 Nadgee NR Melaleuca armillaris dry 0.03 8.31 1.00 201 25.62 152.84 Poona NP Persoonia virgata fleshy 66.30 12.29 8.08 202 25.62 152.84 Poona NP Hakea actites dry 28.69 142.30 0.59 203 25.62 152.84 Poona NP Themeda triandra dry 1.98 6.32 3.92 204 25.62 152.84 Poona NP Cyperus sphaeroideus dry 0.73 4.17 1.91 205 25.62 152.84 Poona NP Eriachne rara dry 1.09 4.89 4.00 206 25.62 152.84 Poona NP Agiortia pedicellata fleshy 15.55 28.07 14.87 207 25.62 152.84 Poona NP Leptospermum polygalifolium dry 0.12 5.15 1.00 208 25.62 152.84 Poona NP Leptospermum trinervium dry 0.05 12.33 1.00 209 25.62 152.84 Poona NP Petrophile shirleyae dry 5.79 16.49 1.62 210 25.62 152.84 Poona NP Eucalyptus latisinensis dry 0.50 6.73 1.00 211 25.62 152.84 Poona NP Melaleuca quinquenervia dry 0.06 7.44 1.00 212 25.62 152.84 Poona NP Melaleuca thymifolia dry 0.02 9.91 1.00 213 34.08 151.07 Royal NP Leptospermum polygalifolium dry 0.18 3.14 1.00 214 34.08 151.07 Royal NP Leptospermum squarrosum dry 0.09 4.34 1.00 215 34.08 151.07 Royal NP Acacia linifolia dry 26.75 2.85 0.53 216 34.08 151.07 Royal NP Lomandra longifolia dry 16.58 0.48 0.02 217 34.08 151.07 Royal NP Petrophile pulchella dry 4.81 45.17 3.96 218 34.08 151.07 Royal NP Dillwynia retorta dry 6.43 6.35 1.91 219 34.08 151.07 Royal NP Patersonia glabrata dry 7.05 1.93 0.57 220 43.06 147.10 Snug Tiers NRA Allocasuarina zephyrea dry 2.45 30.70 1.25 221 43.06 147.10 Snug Tiers NRA Themeda triandra dry 3.87 5.17 1.17 222 43.06 147.10 Snug Tiers NRA Lomandra longifolia dry 7.47 0.56 0.02

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Latitudinal gradient in seed defence

223 43.06 147.10 Snug Tiers NRA Bedfordia salicina dry 0.69 2.67 2.67 224 43.06 147.10 Snug Tiers NRA Leptecophylla juniperina fleshy 75.98 15.95 9.69 225 43.06 147.10 Snug Tiers NRA Lepidosperma laterale dry 2.45 2.89 2.06 226 43.06 147.10 Snug Tiers NRA Exocarpos cupressiformis fleshy 32.68 2.04 0.98 227 43.06 147.10 Snug Tiers NRA Callistemon pallidus dry 1.08 15.16 1.00 228 43.06 147.10 Snug Tiers NRA Leptospermum scoparium dry 0.10 5.50 1.00 229 43.06 147.10 Snug Tiers NRA Leptorhynchos squamatus dry 0.16 1.00 1.00 230 43.06 147.10 Snug Tiers NRA Austrostipa aphylla dry 10.36 5.58 5.34 231 43.06 147.10 Snug Tiers NRA Austrostipa pubinodis dry 13.23 3.11 2.93 232 39.02 146.33 Wilsons Promontory NP Xanthorrhoea australis dry 13.52 5.76 0.68 233 39.02 146.33 Wilsons Promontory NP Lepidosperma squamatum dry 4.78 5.29 4.41 234 39.02 146.33 Wilsons Promontory NP Acacia suaveolens dry 18.42 7.23 0.89 235 39.02 146.33 Wilsons Promontory NP Acacia verticillata dry 7.50 1.98 0.49 236 39.02 146.33 Wilsons Promontory NP Coprosma quadrifida fleshy 2.20 7.67 2.25 237 39.02 146.33 Wilsons Promontory NP Hakea decurrens dry 50.77 102.05 0.31 238 39.02 146.33 Wilsons Promontory NP Leptospermum continentale dry 0.07 6.95 1.00 239 39.02 146.33 Wilsons Promontory NP Melaleuca squarrosa dry 0.07 14.53 1.00 240 39.02 146.33 Wilsons Promontory NP Kunzea ambigua dry 0.04 6.83 1.00 241 39.02 146.33 Wilsons Promontory NP Wprom 10 Eucalyptus dry 0.24 23.98 1.00 242 39.02 146.33 Wilsons Promontory NP Eucalyptus baxteri dry 0.95 28.48 1.00 243 39.02 146.33 Wilsons Promontory NP Cassytha melantha fleshy 71.50 6.85 1.92 244 39.02 146.33 Wilsons Promontory NP Acacia melanoxylon fleshy 16.19 4.34 0.97 245 17.14 145.82 Wooroonooran NP Allocasuarina littoralis dry 3.55 38.84 1.41 246 17.17 145.83 Wooroonooran NP Pittosporum venulosum fleshy 14.64 5.11 0.58 247 17.17 145.83 Wooroonooran NP Hibbertia scandens fleshy 8.64 6.24 1.07 248 17.17 145.83 Wooroonooran NP Smilax australis fleshy 29.60 0.69 0.10 249 17.17 145.83 Wooroonooran NP Pittosporum revolutum fleshy 14.27 3.06 0.50 250 17.17 145.83 Wooroonooran NP Ficus benjamina fleshy 0.25 3.57 1.00

219

Latitudinal gradient in seed defence

220

Conclusions

221

Conclusions

My thesis has contributed to our understanding of seed removal by animals in three key ways:

1) by closing the gaps between data, intuitive ideas and theories; 2) by enhancing integration

of replicated studies at a macro-ecological scale; and 3) by extending association across

biomes and taxonomic groups.

The most important findings of this thesis are:

(1) Animal body mass is negatively related to ingested seed size across vertebrate taxa,

showing for the first time that the loss of large animals will not only have negative effects on

the dispersal of large seeds but could also negatively impact on the dispersal of small seeds.

(2) Fleshy-fruited species are more frequently found towards the tropics and in wet, warm

and stable environments. Importantly, plant reproductive strategies are more strongly associated with conditions during the parts of the year in which plants grow most actively rather than with conditions during the harshest parts of the year.

(3) Neither rates of seed predation on natural seeds nor physical investment in tissues surrounding seed reserve are higher towards the tropics. Interspecific pre-dispersal seed predation and seed investment are actually greater towards higher latitudes. These results add

to the growing body of evidence that casts doubt on the generality of the latitudinal gradient

in biotic interactions, and our current understanding of the factors shaping global patterns in

biodiversity.

222

Conclusions

Implications for the role of fleshy tissues in seed removal

My work indicates that fleshy tissues could be of particular importance to the dispersal of

large-seeded species. The seeds of fleshy-fruited species are generally larger than those of

dry-fruited species (Willson et al., 1990; Chen et al., 2004; Bolmgren & Eriksson, 2010). I

propose that fleshy fruits are a dispersal attribute that maintains sufficient dispersal of large- seeded species, and that fleshy fruits allow large endozoochorous seeds to achieve similar dispersal distances as species with small seeds. First, fleshy fruits facilitate animal ingestion

(Snow, 1971; Stiles, 2000). My comprehensive data compilation of animal-seed interactions supports this point by demonstrating that animals ingest more species of fleshy-fruited seeds than of dry-fruited seeds (Chapter 1). Given that animal ingestion transports seeds further than do other dispersal modes (Thomson et al., 2011), I suggest fleshy fruits increase seed dispersal distance, by partially compensating the reduced dispersal ability of large seeds (e.g.

Greene & Johnson, 1993). Second, Chapter 1 show that when ingested seeds are from fleshy fruits, larger animals generally ingest larger seeds. This finding is consistent with the idea that fleshy fruits promote active seed-seeking (Gautier-Hion et al., 1985) and facilitate seed dispersal by large animals (Jordano, 1995). Larger animals disperse a greater diversity of seed species across greater ranges of seed size (Figures 6 and 2 in Chapter 1, respectively), in greater seed numbers (Bakker & Olff, 2003), and to greater distances (Nathan, 2006; Nathan et al., 2008). My thesis, in combination with this evidence, suggests that, although all seeds benefit in terms of dispersal from fleshy fruits, large seeds benefit most.

Not all fleshy fruited species have large seeds, and not all large seeds are covered with fleshy tissues. It seems unlikely that the fleshy tissues of fruits develop solely to facilitate dispersal.

My results for the Australian flora (Chapter 4) show that fleshy-fruited seeds suffer lower levels of pre-dispersal seed predation than do dry-fruited seeds, consistent with the pattern

223

Conclusions

found in Mediterranean flora (Herrera, 1987). This quantitative analysis supports the

“defence scenario” initially proposed by Mack (2000) where fleshy pulp originated as a seed

defensive structure and secondarily became an attractant and rewards to facilitate seed

dispersal. This scenario is particularly pertinent to fleshy fruits that are covered with

vertebrate-hostile structures (such as the stinging hairs on the fleshy fruits of stinging tree

Dendrocnide moroides, see Chapter 3) and for fleshy fruits that capsulate winged seeds (e.g.

Parmentiera species). My results (Chapter 4) show that fleshy pulp is not only an effective defense, but it also requires a relatively low investment of biomass against seed predators

(Chapter 4). In this way, my thesis shows that a higher proportion of fleshy-fruited species

(Chapter 2) is a promising reason for the lower level of pre-dispersal seed predation (Chapter

3) and the reduced biomass investment in defences at lower latitudes (Chapter 4).

Latitudinal gradient in biodiversity

Several theories have been put forward to explain species coexistence and the latitudinal gradient in plant diversity (reviewed in Terborgh et al., 2002; Wright, 2002; Mittelbach et al.,

2007). It has been suggested that more intense and more specialized biotic interactions at lower latitudes cause a positive feedback that generates and maintains a latitudinal gradient in biodiversity (Rohde, 1992). However, several recent studies have found that both mutualistic and antagonistic interactions between plants and animals (e.g. pollination, frugivory, herbivory) are not more intense or more specialized towards the tropics (Ollerton & Cranmer,

2002; Vamosi et al., 2006; Almeida-Neto et al., 2008; Moles et al., 2011; Poore et al., 2012;

Schleuning et al., 2012; Kozlov et al., 2015). My findings on the interactions between seeds and seed predators add empirical evidence to this body of literature.

224

Conclusions

The “population recruitment curve” predicted by the Janzen-Connell hypothesis yields a

recruitment peak that is a product of seed density at a given distance from the maternal plant,

and the probability of seed survival at that distance (Figure 1a; Janzen, 1970). Seed mortality

due to seed predation in the vicinity of maternal plants has been suggested as a mechanism to

facilitate species coexistence, and this effect was hypothesized to be stronger towards the

tropics (Figure 1a; Janzen, 1970; Connell, 1971). However, my empirical findings showed

that neither seed predation (Chapter 3) nor seed defence (Chapter 4), are stronger at low

latitudes. In fact, I found that pre-dispersal seed predation was more intense at high latitudes

(Chapter 3). That is, empirical data do not support the idea that a downward-shift in the seed

survivorship curve (Fig. 1a) is responsible for the increased distance between the peak of

population recruitment curve and the maternal plant (Figure 1a). My results are consistent

with recent global syntheses showing that neither density- nor distance-dependent mortality is

stronger in tropical latitudes (HilleRisLambers et al., 2002; Hyatt et al., 2003; Comita et al.,

2014). Thus, the latitudinal gradient in plant diversity cannot be explained by hypotheses

invoking interactions between plants and their above-ground pests. Geographic variation in

below-ground biotic interactions remains relatively unexplored, so it is possible that the

Janzen-Connell hypothesis could be supported by interactions between plants and the soil

biota. Consistent with this idea, a recent study demonstrated that fungal pathogens, rather

than insect herbivores, caused the changes in plant diversity and species composition in a

tropical community (Bagchi et al., 2014). The global patterns in the effects of soil microbes are next foci to test the biotic interactions hypothesis.

One possible mechanism through which tropical ecosystems might maintain a large number of coexisting species is through extended seed shadows. Chapter 2 proposes that seed

dispersal distance may be greater towards the tropics, because fleshy fruits tend to be

225

Conclusions associated with long dispersal distance by animal ingestion (Thomson et al., 2011; Tamme et al., 2014), and because there are a greater proportion of species bearing fleshy fruits in the tropics (Chapter 2). The resulting extended seed shadow would be associated with a recruitment peak further from the maternal plant, and thus increased species coexistence

(Figure 1b). In order to test this hypothesis, we would need to quantify the latitudinal gradient in seed dispersal distance. To date, only two studies are available to quantify the latitudinal gradient in seed dispersal distance and they gave differing outcomes. Dispersal distance was greater towards low latitudes for ant-dispersed seeds of multiple species (Gómez & Espadaler,

2013); while dispersal ability (as modelled by inverse terminal velocity) was greater towards high latitudes for a wind-dispersed species (Riba et al., 2009). This is clearly a promising area for further works.

A second possibility is that greater seed production in the tropics (as found by Moles et al.,

2009) could result in greater dispersal distances, and thus recruitment peaks further from the maternal plant, and thus greater distances between conspecific plants (Figure 1b; also Figure

2 in Janzen, 1970). The traditional Janzen-Connell hypothesis could only generate small distances between conspecific individuals, thus only providing a potential explanation for the coexistence of a small number of plant species (Hubbell, 1980). By contrast, my model based on seed shadow could potentially generate much larger gaps between conspecific species, depending on the dispersal distances involved.

226

Conclusions

Figure 1. As the distance from maternal plant increases, the seed density curve (I) declines while the probability of seed survival (P) increases, yielding a population recruitment curve

(PRC) which represents the likelihood of seedling establishment. The peak of PRC is where a new adult is most likely to appear, and is calculated as the product of seed density and the probability of seed survival. Because of greater losses to seed predation in the tropics,

Janzen’s (1970) model (a) proposes that the probability of seed survival close to the maternal

tree at the tropical latitudes (Ptropical) is lower than is the probability of seed survival at a

similar distance from the maternal tree at the temperate latitudes (Ptemperate), resulting in

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Conclusions

PRCtropical shifting further away from the maternal plant than PRCtemperate. This greater

distance from conspecific individuals promotes species coexistence, and explains the high

plant diversity in the tropics. However, my thesis shows that seed predation is not stronger in

the tropics, and therefore fails to support a difference between Ptropical and Ptemperate. Instead,

my thesis suggests that tropical species may achieve extended seed shadows by dispersing

seed further and/or producing more seeds, than do temperate species. In the new model (b),

the extended seed shadow (Itropical) produces a shifted peak of PRCtropical, which is a greater

distance from the maternal plant, resulting in an increased distance between conspecific individuals.

Conclusion

Science is self-correcting. Many current ideas in ecology originated in the 18th and 19th

centuries, and gained theoretical reinforcement in the latter half of the 20th century (Jax,

2001). As a proportion of these theories took root and flourished in observations taken within certain geographic ranges or certain taxonomic groups, their prominence at large scales needs to be revisited (Keith et al., 2012). The process of science, from observation to hypothesis,

and from hypothesis to theory, operates well only when hypotheses are rigorously tested. For

macro-ecological predictions about biotic interactions, ecologists must continue to conduct

good science by performing experiments at appropriate scales of space and diversity, and by

testing ideas against the realities that life presents. From knowledge to wisdom, the avenues

of long-held ideas need to be constantly tested and the future is now.

228

Conclusions

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Drawing by Si-Chong Chen.

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