The Influence of Land Management Practices on the Abundance and Diversity of Fall-Blooming Asteraceae and Their Pollinators

THE INFLUENCE OF LAND MANAGEMENT PRACTICES ON THE ABUNDANCE AND DIVERSITY OF FALL-BLOOMING ASTERACEAE AND THEIR POLLINATORS

Julie Jung

Professor Joan Edwards, Advisor

A thesis submitted in partial fulfillment of the requirements for the Degree of Bachelor of Arts with Honors in Biology

WILLIAMS COLLEGE

Williamstown, MA

May 2015 TABLE OF CONTENTS

I. Acknowledgements ...... 5

II. Abstract...... 6

III. Introduction...... 7 A. Study Overview...... 7 B. A Global Pollinator Crisis ...... 8 a. Main Drivers of Pollinator Decline...... 11 i. Pathogen Spillover...... 11 ii. Parasites...... 12 iii. Pesticides...... 13 iv. Anthropogenic Land Use Change……………………..……...14 1. Habitat Loss and Pollination Limitation….………..…15 2. Cascades of Decline…………………...……………...15 b. Habitat Loss in New England...... 16 c. Conservation Policy...... 18 d. Study Objective & Hypothesis...... 20 C. Study Organisms...... 23 a. Plants...... 23 i. The Family Asteraceae...... 23 ii. The Genera Solidago and Euthamia...... 24 b. Pollinators...... 27 i. Case Study: Natural History of Apis mellifera………………..27 1. Life Cycle...... 28 2. Anatomy...... 30 3. Pollination...... 32 ii. Case Study: Natural History of Bombus sp...... 33 1. Life Cycle...... 34 2. Anatomy...... 36 3. Pollination...... 37

IV. Methods...... 39 A. Study Site…...... 39 B. Site History...... 39 C. Treatments & Experimental Procedure...... 41 D. Spatial Distribution Study...... 43 E. Pollinator Activity Study...... 45

V. Results...... 48 A. Flowering Stems...... 48 a. Abundance by Treatment...... 48 b. Spatial Distribution Maps of Each Plot...... 52 c. Patch Size...... 57 d. Height of Tallest Plant in Each Quadrat...... 62 e. Floral Diversity, Richness, and Evenness...... 63 f. Rank Abundance Analysis/Dominance Diversity Curve...... 67 B. Pollinator Visitors...... 68 a. Abundance by Treatment...... 68 b. Pollinator Diversity, Richness, and Evenness...... 69 c. Rank Abundance Analysis/Dominance Diversity Curve...... 73 d. Rarefaction Curve...... 75

VI. Discussion...... 76 A. Flowering Stems...... 76 B. Pollinator Visitors...... 80

VII. Conclusion...... 81

VIII. References...... 82

IX. Appendices...... 86

Appendix 1: Spatial Distribution Maps (16 + 4 grouped)...... 86

Appendix 2: Pollinator Key...... 95 Part A: Family Apidae (6)...... 95 Part B: Family Halictidae (2)...... 100 Part C: Family Vespidae (3) ...... 101 Part D: Unknown Wasps (3) ...... 104 Part E: Family Syrphidae (8) ...... 107 Part F: Family Tachnidae (1) ...... 111 Part G: Family Hesperiidae (1) ...... 112 Part H: Family Pieridae (1) ...... 113 Part I: Family Membracidae (1) ...... 114 Part J: Family Tettifoniidae (1) ...... 115 Part K: Family Choccinelldae (1) ...... 116 Part L: Unknown Beetles (3) ...... 116 Part M: Family Formicidae (1) ...... 118 Part N: Unknown Spider (1) ...... 118 Part O: Unknowns (15) ...... 119

Appendix 3: R Scripts...... 126 Part A: FloweringStemsbyTreatment.R...... 126 Part B: SpatialDistribAllPlots.R ...... 136 Part C: Clumping.R ...... 141 Part D: Height.R...... 152 Part E: floweringstemdiversity.R (includes rarefaction plot)...... 156 Part F: RankAbundanceStems.R...... 168 Part G: PollinatorVisitors.R...... 174 Part H: pollinatordiversity.R (includes rarefaction plot) ...... 184 Part I: RankAbundanceVisitors.R...... 202

ACKNOWLEDGEMENTS

A million thanks to Professor Joan Edwards for selflessly sharing her love of Ecological systems. Every hour of her counseling and company in the field made me positive I want to make a career out of being a buglady. Her teachings at times made the top of my head tingle and her endless enthusiasm and overflowing knowledge about pollinator plant dynamics constantly kept me curious. Her warmth and wisdom are truly inspiring. Moreover, she understood my sorrow when I discovered that when bumble bees buzz in petri dishes it sounds like they’re yelling for help and when butterflies are euthanized their struggle makes their scales shed slowly.

This section couldn’t be complete without a word about Professor David Smith. Even when I would stop by without notice outside of his normal office hours (when he was technically on sabbatical) desperately seeking R-help, he would happily help me write and understand my code. I thank him for countlessly going above and beyond his call of duty.

I thank Drew Jones and his Hopkins Memorial Forest Fall 2014 crew for laying out 2704 flags at the weather station field site in preparation for collecting spatial distribution data. I also appreciate the help of the Fall 2014 Ecology Class at Williams College for their lab hours spent helping to survey plants in our plots. In particular, I want to thank Julia Matejcek, William Schmidt, Sophia Schmidt, Erica Bucki, Ben DeMeo, and Valeria Pelayo for volunteering extra time to help survey Asteraceae in HMF and Eirann Cohen for her clumping quantification and analysis. Lastly, many thanks to my friends and family with whom I’ve shared too many “fun facts” about pollinators. Thank you for loving my lunacies.

ABSTRACT

Fall blooming Asteraceae provide an important resource for overwintering pollinators, yet field species are increasingly rare because of reforestation and changes in land-use practices (Foster and Aber 2004). Here we test the impact of mowing schedules on the abundance and diversity of flowers and their pollinators. Four mowing treatments- early annual (EA), early biennial (EB), late annual (LA), and late biennial (LB)- were randomly assigned to blocks of four plots, grouped by similarity in location and vegetation. Conducted plant surveys of the inner quadrats of each plot show that early mowing results in significantly fewer flowering stems, smaller patches, and shorter plants. Simultaneously filmed stems of Solidago rugosa show that early mowing yields significantly fewer insect visitors, fewer species attracted, and lower diversity (H’) of insect visitors. These data indicate that delaying mowing until October is an effective and feasible land management strategy that would yield significantly more floral resources, more pollinator abundance, and higher diversity of insect visitors.

INTRODUCTION

Study Overview

Members of the Asteraceae family are a dominant component of the fall flora in

New England. They are a beautiful and an arguably undervalued source of biodiversity and overwintering resources for pollinators. In the context of the global pollinator crisis, however, both benefactors of this plant pollinator mutualism are in danger. There are many drivers of pollinator decline that are believed to act simultaneously and perhaps synergistically, but anthropogenic land use change is considered the main cause and predicted to be the greatest cause of biodiversity losses in the future. Since the open fields preferred by many species of fall blooming Asteraceae require periodic disturbance to maintain their optimal habitat (Pavek 2011), New England’s history of habitat loss through reforestation and the rise of the suburban lawn are especially concerning because it decreases available habitat for native field Asteraceae (Foster and Aber 2004).

We hypothesize that land management practices such as mowing schedules strongly influences the density and diversity of flower and pollinator species within an existing field. The study aims to examine the influence of mowing schedules on abundance and diversity of flowers and then focuses on the impact of mowing on pollinators on Solidago rugosa. We tested four different mowing treatments: Early

Annual (EA), Early Biennial (EB), Late Annual (LA), and Late Biennial (LB). These treatments were randomly assigned to blocks of four plots, grouped by similarity in location and vegetation. We conducted plant surveys of the inner quadrats of each of the

16 plots in order to show that early mowing results in fewer flowering stems, smaller

7 patches, and shorter plants. We also simultaneously filmed stems of S. rugosa in order to show that early mowing attracts fewer insect visitors, fewer insect species, and lower diversity (H’) of insect visitors. By comparing the effects of mowing times on the abundance and diversity of flower stem production and pollinator activity, we hope to motivate more productive land use and the conservation of biodiversity in New England fields.

A Global Pollinator Crisis

Biotic vectors such as insects and other animals pollinate the majority of the

352,000 species of angiosperms and the remaining minority of flowering plants uses abiotic pollen vectors like wind (Ollerton et al. 2011). The proportion of animal- pollinated angiosperm species ranges from a mean of 78% in temperate-zone communities to 94% in tropical communities (Ollerton et al. 2011). More than 80% of wild plant species (Potts et al. 2010) and 75% of cultivated plant species (Klein et al.

2007) directly depend on insect pollination, particularly native bees, for fruit and seed set.

Pollinators similarly depend on the plants they pollinate for the nutrients they need to survive: pollen provides the protein in a bee’s diet while nectar is a source of carbohydrates (Free and Butler 1968, Willmer 2011). Uncoupling this mutualism could have drastic effects at a community level. Numberous studies report both global and regional declines in pollinators and the plants they pollinate (Terzo and Rasmont 1995,

Biesmeijer et al. 2006, Kosior et al. 2007, Grixti et al. 2009, Potts et al. 2010, Cameron et al. 2011, Meeus et al. 2011, Bommarco et al. 2012, Thomann et al. 2013).

The decline of pollinator abundance and diversity could drive a corresponding decline in pollination services for wild flowers and cultivated crops, thus further reducing floral resources for pollinators (Potts et al. 2010). This negative feedback between bees and the plants they pollinate has the potential to bring about disastrous ecological and economic consequences. Both local-scale monitoring programs and larger-scale synthesis reports point out that in the past sixty years there have been significant global declines of pollinators and the plants they pollinate.

The most notable disturbances are the absence of many prominent bee taxa from their former ranges and significant reductions in species richness and functional composition. Honey bees have been in steady decline since the 1940s and CCD is just the latest in a string of challenges. Population reductions of North American Bombus were first identified in 1998 when it was discovered that the Franklin Bumble bee Bombus franklini had become increasingly rare in the past decade (Williams et al. 2014). Bombus franklini was the first North American bumble bee to show up on the IUCN Red List of

Threatened Species and has not been sighted since 2006; its rapid decline has spurred the collection of baseline data for other North American Bombus species (Williams et al.

2014). As a result, recent studies in the US and Canada have found that up to half of

North American Bombus may be at risk, but many species show varying levels of decline

(Williams et al. 2014).

Not all bees in all regions of the world are created equally, and thus not all bees in all regions of the world are in decline to the same degree or at all. Since bee species differ so much in life history traits such as seasonal activity, preferred food plants, colony

9 productivity, and habitat use, we see significant variability in their responses to environmental stressors (Williams et al. 2014). In fact, some species such as the Common

Eastern Bumble bee B. impatiens and the Cryptic Bumble Bee B. cryptarum have become increasingly common or expanded their former ranges (Williams et al. 2014). One extensive long-term study of orchid-bees (Euglossini) in Panama found no aggregate trend in abundance from 1979 to 2000 (Roubik 2001). An analysis of Food and

Agriculture Organization (FAO) data reveals that the global population of managed honey bee hives has actually increased nearly 45% in the last half century (Aizen and

Harder 2009).

The proper assessment of pollinator-plant interactions for future planning to enhance and sustain pollination services has proven challenging mainly because of a lack of baseline and long-term data (Grixti et al. 2009). Data on pollinator population trends or deficits are notoriously difficult to adequately record and the ones we have vary both temporally and spatially (Ghazoul 2005, Winfree et al. 2009, Williams et al. 2014). To date, there is no standard methodology, core protocol, or coordinated monitoring program for documenting pollinator occurrence and abundance (Potts et al. 2010). Moreover, often inadequate taxonomic expertise, inherently biased sampling techniques, and the lack of consistently reliable identification guides have led to poorly documented insect fauna in most regions (Willmer 2011). There is a desperate need for more long-term, continuous studies in order to gain meaningful insights on pollinator population shifts in nature

(Roubik 2001).

Main Drivers of Pollinator Decline

It is unlikely that a single stressor is the sole cause of pollinator population decline. Recent declines in both honey bee (Apis) and bumble bee (Bombus) populations have been attributed to a combination or threats or multiple interacting causes. The

‘perfect storm’ of stressors might include pathogen spillover, pesticides, climate change, the introduction of exotic and invasive species, and land management stressors that lead to habitat loss and fragmentation.

(i) Pathogen Spillover

In the cramped conditions common to industrial beekeeping operations, bees are particularly vulnerable to diseases, and they may spread pathogens and parasites to wild bees when they escape from greenhouses- a phenomenon known as pathogen spillover.

Studies have found that wild bumble bees foraging near greenhouses with managed bumble bees have higher incidence of disease than do wild bumble bees foraging far from greenhouses with managed bumble bees and the decline of some bumble bee species correlates with the density of vegetable greenhouses in the US and Canada (Williams et al. 2014).

Cornell University researchers estimated in 2000 that the economic value of bee pollination services amounts to $14.6 billion in extra yield and improved crop quality

(Stokstad 2007). In the past two decades -starting in the early 1990s- as the United States started importing cheap honey from abroad, companies in Europe, Israel, and Canada began to develop commercial insectary techniques for rearing bumble bees for crop

11 pollination and large beekeeping operations have begun to make their income from renting hives to farmers (NRC 2007, Stokstad 2007). Since then, Apis mellifera, Bombus impatiens and B. occidentalis have been the main species used commercially (NRC

2007).

The rise of industrial-scale beekeeping—or the trucking of hundreds of thousands of hives around the country—has made colonies more vulnerable. The bumble bee species are reared and deployed for pollination at densities as high as n=23,000 per greenhouse (Morandin et al. 2001). The stress of these commercial pollination practices can lead to genetic loss from artificial queen insemination and transportation stress from long hours in big trucks with cramped quarters (Stokstad 2007). Moreover, the introduction or deliberate transportation of bumble bees beyond their natural range to enhance crop pollination is a cause for concern because non-native bumble bees can in turn lead to competition with native bees, introgression with related wild species, transmission of parasites or pathogens to native organisms, or changes in seed set of native plants (Goulson 2010).

(ii) Parasites

Bumble bees and honey bees suffer from infestation by several parasites. The honey bee’s “number 1 public enemy” is the mite, Varroa destructor (Stokstad 2007).

Varroa destructor mites and Acarapis woodi or tracheal mites were both introduced in the 1980s from overseas (Stokstad 2007). These parasitic mites feed on the blood of bees, particularly at larval stages. Adult mites also carry some lethal invasive pathogens or

12 disease-causing agents linked to Colony Collapse Disorder (CCD) such as Israeli Acute

Paralysis Virus (IAPV) and Deformed Wing Virus (DWV) (Cox-Foster et al. 2007). A metagenomic study of the microflora in CCD ridden hives identified candidate pathogens, screened for significance of association over three years, and found the highest correlation was with Israeli Acute Paralysis Virus (Cox-Foster et al. 2007).

Symptoms of IAPV include dead bees in the hive, an incremental decline in worker population, and pest invasion (Cox-Foster et al. 2007). Both healthy hives and unhealthy hives have mites (Cox-Foster et al. 2007). Miticides have been suggested as a potential chemical solution against mites, but mites have a very rapid rate of reproduction

(Maori et al. 2009). A single female Varroa mite can produce five or six generations of mites during its lifetime (Maori et al. 2009). As a result, mites develop miticidal resistance very quickly and honey bees do not reproduce with efficiency to keep up.

Since the rise of globalization brought new bee pests like Varroa mites in the late 1980s, colonies have experienced increased mortality. In fact, the number of honey-producing colonies dipped by nearly 25 percent at that time. These new pathogens and pests in the

1980s were certainly additional stresses.

(iii) Pesticides

Pesticides also act as an additional stressor to both bumble bees and honey bees

(Schacker 2008). A new study shows that every batch of pollen that a honey bee collects has at least six toxic pesticides in it (Spivak 2013). A new class of nicotine-based compounds called neonicotinoids was first introduced as pesticides in 1992 (Stokstad

2007). High doses of such compounds, like in ground applications, get into a flowers nectar and pollen and the neurotoxin makes the bee twitch and die (Spivak 2013). Low doses interfere with a bee’s ability to navigate back to the hive; the pesticide intoxicates the bee so she can’t find her way home (Stokstad 2007).

(iv) Anthropogenic Land Use Change

The newly considered main drivers of worldwide pollinator and crop production crises are increasingly human induced changes. Bee enemies, diseases, and die-offs have been recorded since the 18th century, perhaps earlier (Thorley 1744). Challenges notable enough to be referenced in texts changed from the bee moth Tinea mellonella and dysentery in the 19th century to the protozoan parasite Nosema apis, viral infections, and parasitic mites like Acarpis woodi by mid 20th century (Betts 1951, Bailey 1963, Free and

Butler 1968). These concerns have been chalked up as part of the natural ebb and flow of pollinator population dynamics. In the past 60 years, however, a new pattern has emerged.

In recent times, between 50% (Vitousek et al. 1997) and 75% (Ellis and

Ramankutty 2008) of the earth’s land surface is converted to human use. For example, both pathogen spillover from managed colonies and pesticides, are human induced issues.

Climate change and the introduction of exotic and invasive species, other major contributors to pollination decline, are also due to anthropogenic presence (IPCC 2013,

Williams et al. 2014). These drivers are believed to act simultaneously and perhaps synergistically, but anthropogenic land use change is considered the main cause of

14 current pollinator decline and predicted to be the greatest cause of biodiversity losses in the future (Winfree et al. 2009, Potts et al. 2010).

1. Habitat Loss and Pollination Limitation

Habitat alteration, fragmentation and loss are significant disturbance types to wild bee abundances and species richness (Kearns et al. 1998, Winfree et al. 2009). In a meta- analysis of bees’ responses to various community disturbances, a quantitative review of

54 studies of 89 plant species found pollination limitation to be the most frequent proximate cause of reproductive impairment of wild plant populations in fragmented landscapes (Winfree et al. 2009, Potts et al. 2010). A similar meta-analysis of 23 studies found distance from natural habitat due to habitat loss and/or habitat conversion had a strong significant negative effect on the species richness and abundance of wild bees

(Ricketts et al. 2008). Such studies suggest that habitat loss reduces bee diversity and abundance (Potts et al. 2010). There may be ways for short-term compensation for poor pollinator (e.g. clonal propagation), but these cannot compensate in the long-term for a chronic loss of pollination service (Potts et al. 2010).

2. Cascades of Decline

There is significant potential for global pollinator decline to propograte community-level cascades of decline (Chapin et al. 1997). The loss of some elements of the biota often leads to the subsequent loss of other species that directly or indirectly rely upon them; thus, pollinator decline has grave implications for the plant species that rely

15 on pollinators to propagate (Biesmeijer et al. 2006). Recorded observations for bees, hoverflies, and plants from Britain and the Netherlands suggest that outcrossing plant species that depend on declining pollinators have themselves declined relative to other plant species (Biesmeijer et al. 2006).

Anthropogenic changes in habitats and climates have caused loss of biodiversity among both vertebrate and insect taxa (Biesmeijer et al. 2006). Possible consequences of biodiversity loss include shifts in water and nutrient dynamics, trophic interactions, or disturbance regimes that ultimately affect the structure and functioning of connected ecosystems (Chapin et al. 1997). As a result, differences in environmental sensitivity make ecosystems more unstable and vulnerable to change and potentiate community- level cascades of decline (Chapin et al. 1997).

Habitat Loss in New England

The open fields preferred by many species of fall blooming Asteraceae require periodic disturbance to maintain their optimal habitat (Pavek 2011). Without mowing or fire or storm damage, these fields will transition into small forests and eventually mature woodlands. In New England, increasing reforestation of New England open fields since the turn of the 20th century (Figure 1) decreases available habitat for native field

Asteraceae (Foster and Aber 2004).

Figure 1: Comparisons of forested areas in Massachusetts in 1830 and 1999 show open field habitat is increasingly scarce (Foster and Aber 2004).

Frequent mowing of yards and fields also prevents the growth of these species because they are never permitted to grow, flower, and set seed. The concept of a lawn or a managed green space first became popular with the northern European aristocracy in the Middle Ages (Robbins 2007). Edwin Beard Budding, an English engineer, invented the mowing machine in 1830 but in America it took until the rise of suburbia in the post-

World War II economic expansion for landscape aesthetic to catch on. Abraham and

William Levitt’s Levittown, NY, the first and one of the largest mass produced suburban developments, became a symbol of post-war suburbia. The houses intentionally took up only twelve percent of the lot so that the rest of the space could be landscaped into manicured lawns (Hancock 2011). Among the many drawbacks of lawns are an increased investment of inorganic fertilizers, pesticides, herbicides, fungicides and a high water requirement (Robbins 2007). Moreover, the common use of introduced species and monoculture of plants both reduce biodiversity. The suburban social pressure to mow down weeds in favor of manicured greens has contributed to significant habitat losses for beneficial native perennial blooming plant species.

Conservation Policy

Adopting landscape management practices that encourage habitat heterogeneity can have enormous consequences for pollinator conservation. There has been limited legislative action taken by the USDA or the US EPA regarding pollinator decline. US policy and recent projects such as Yellowstone to Yukon Conservation Initiative seek to connect protected spaces and remediate ecological losses due to habitat fragmentation,

18 but pollinator conservation is still mostly predominantly a people’s campaign (Hancock

2011). One direct and easy way to combat bee decline is reduction in pesticides through judicious and targeted use. The organic food movement has been slowly gaining speed since the early 1900s, and now the pollinator epidemic has added one more reason to join the campaign against pesticides.

A second strategy is habitat enhancement. Cover crops, flower-rich field margins and crop borders, and hedgerows with perennial blooming plants would help break up the food desert for bees (Spivak 2013). Native and pollinator friendly plants that are good sources of nectar and pollen such as red-clover, foxglove, bee balm, joe-pye weed, and goldenrod (Spivak, 2013). These species would attract a diversity of pollinator populations and provide foraging options for bees with extended flight times. A study in northern Canada found that canola fields near other uncultivated fields had greater yields than those farther from uncultivated fields (Morandin and Winston 2005). The results maintain that farmers would actually increase profits if they allowed 30% of their lots to be flower-rich fields, since the more abundant and diverse bee community would increase pollination services and subsequent yield in the remaining 70% of the field area

(Morandin and Winston 2005).

Study Objective & Hypothesis

In this study we test the hypotheses that early mowing decreases floral resources

(lower flowering stem abundance, less clonal clumping, shorter stems, less diversity, less species richness, and less evenness) and pollinator resources (lower visitation rate, less diversity, less species richness, and less evenness). Conversely, late mowing increases the flower/herb resources and diversity for pollinators and thus is an important tool for helping to prevent pollinator decline.

In the face of habitat loss and other sources of pollinator decline, it is important to consider and identify pollinator patterns when determining conservation needs. Mean weight per honey bee colony tracked over time shows that increase in colony weight corresponds with seasonal increases in flower bloom (Mattila and Seeley 2007).

Underlined data are added to emphasize the increase in colony weight associated from bloom events (Figure 2). For instance, the increase from September to October is likely to depend on resources from late blooming flowers such as asters and goldenrods, which bloom at a crucial time for pollinators just prior to the freeze of wintertime. Pollen and nectar from these flowers provide pollinators with provisions to over-winter. Only colonies with sufficient resources from late blooming flowers successfully survived the winter (Mattila and Seeley 2007). If caretakers and property owners mow early, these resources can be diminished and the missed potential is enormous. Since pollinators depend on fall blooming flowers to over-winter, a mowing treatment drastically decreases the pollinator’s chances for survival through the winter.

Figure 2: Mean weight per honey bee colony tracked over time shows that increase in colony weight corresponds with seasonal increases in flower bloom. Underlined data are added to emphasize the increase in colony weight associated from bloom events (Mattila and Seeley 2007).

My thesis study aims to examine the influence of mowing schedules on abundance and diversity of flowers and then focuses on the impact of mowing on pollinators on Solidago rugosa. I compare the number of flowering stems and pollinator visitation to Solidago rugosa species in response to four different mowing treatments:

Early Annual (EA), Early Biennial (EB), Late Annual (LA), and Late Biennial (LB).

Goldenrod’s clonal growth pattern predicts clumping regardless of plot location or mowing pattern, and the size of the clumps should increase with time. We expect late- mow plots have more clumping than early plots, and plots mowed every other year to have more clumping than plots mowed annually. In other words, the greatest increase in

21 clumping size (an important indicator of growth and resiliency as a pollination system) is expected between early annual mow and late biennial mow plots. I will also collect data to compare diversity statistics such as Evenness, Richness, and Shannon-Wiener

Diversity Indices. In comparing the effect of various field maintenance strategies, I hope to determine how mowing frequency and timing influence pollinator and Asteraceae abundance, distribution, and diversity.

Study Organisms:

(i) The Family Asteraceae

The “sunflower” family Asteraceae, also previously referred to as Compositae, boasts the largest number of described species of any plant family in the world or about

10% of all flowering plant species (Krupnick and Kress 2005). More than 23,000 currently accepted species are spread across 1,620 genera and 12 subfamilies and occur on all continents except Antarctica (Krupnick and Kress 2005, Judd et al. 2008, WCSP

2011). In comparison, Orchidaceae, the next biggest family, contains about 19,500 species, and the third largest, Fabaceae, approaches 18,000 (Judd et al. 2008). Although

Asteraceae is monophyletic and fairly well defined, there is much variety among its members (Krupnick and Kress 2005). Species grow in nearly every type of habitat and vary from annual herbs to long-established vines or trees (Krupnick and Kress 2005). The most evident characteristic of Asteraceae is their inflorescence, a specialized capitulum composed of many (the number can range from 1 to more than 1,000) individual ray or disc florets that share the same receptacle and function to attract pollinators (Nakagawa and Ito 2014).

Twenty-six different species of asters are found in Berkshire County (Weatherbee and Museum 1996). Those present at the site were Symphyotrichum lateriflorum (Calico

Aster), Symphyotrichum cordifolium (Heart-leaved Aster), and Symphyotrichum puniceum (Purple-stemmed Aster). The most common aster variety at the study site was

Symphyotrichum lateriflorum. S. Lateriflorum, or the Calico Aster, is aptly named for its disc florets that change color from pale yellow or pink to red or purple, resulting in a

23 patchwork of light and dark centered heads (Semple et al. 2002). This aster blooms from

September to October and stands 6-9 cm tall with small heads 1.0-1.3cm wide (Tal

2010).

(ii) The Genera Solidago and Euthamia

Within the Asteraceae family, Solidago and Euthamia, commonly called goldenrod, are genera of about 100 different species of flowering plants (Marshall

Cavendish 2001). From late summer to fall (July through October) in North America, these perennial dicots may bloom in vibrant yellow arches with terminal clusters composed of numerous homogeneously colored tiny individual florets (Mani and

Saravanan 1999, Pavek 2011). Goldenrod often have a bad reputation as a “weedy” plant due mostly to their aggressive rhizomatous growth, which can make them difficult to control but able to rapidly colonize disturbed sites in large dense patches (Pavek 2011).

In stable environments, however, goldenrod seldom achieve problematic densities

(Werner et al. 1980, Whitson et al. 2004).

Goldenrod is also an easy scapegoat for fall allergies or hay fever because people see the bright yellow flowers, which co-bloom with the green inconspicuous wind- pollinated flowers of ragweed (Ambrosia sp., Asteraceae), and blame them for their sniffles. In reality, the pollen of Ambrosia is a potent allergen, whereas the showy flowers of goldenrods do not spread their pollen in the wind, but rely on pollinators like solitary wasps, pollen-eating beetles, honey bees, and bumble bees to pollinate (Pavek

2011). Instead of having small, dry pollen pieces designed for slight air currents to carry,

24 goldenrod pollen is sticky, large, and specifically designed to affix to insect pollinators

(Marshall Cavendish 2001). In addition, goldenrod species have short floral tubes, which make nectar easily accessible to more than 144 species of different visitors (Lovell and

Company 1926).

The species of goldenrod present at our experimental site in were Solidago altissima (Late Goldenrod), Solidago canadensis (Canada Goldenrod), and Solidago gigantea (Smooth Goldenrod), Solidago rugosa (Wrinkle-leaved Goldenrod), and

Euthamia graminifolia (Grass-leaved Goldenrod). Solidago altissima, Solidago

Canadensis, and Solidago gigantea have similar morphologies and are sometimes difficult to tell apart. All three are reported to bloom in late summer and fall and display narrowly lanceolate stem leaves with three prominent parallel veins that are pubescent on the underside of the leaf. Solidago gigantea, however, are typically glabrous and glaucous below the capitulescence and have a waxy bloom on the stem. Solidago altissima are generally taller and more pubescent than Solidago Canadensis along the stems and leaf undersides; whereas S altissima is densely pubescent along the entire underside, S. Canadensis are pubescent mostly along the main veins. Solidago rugosa and Euthamia graminifolia both only have one prominent vein. Solidago rugosa stems are generally stout and densely pubescent with numerous flower heads crowded in one- sided arrays on its branches. The leaves of Euthamia graminifolia are slim and grass-like

(Figure 3).

Figure 3: Bombus on Euthamia graminifolia (photo by David Smith).

(i) Case Study: Natural History of Apis mellifera

The best-documented evidence of specific pollinator decline can be seen in the western or European honey bee Apis mellifera (NRC 2007). Agriculture depends greatly on Apis mellifera for its pollination services. Honey bees account for 80% of all insect pollination (Huston 2013) and support about a third of human food by pollinating some

95 types of fruits and vegetables in the United States (Kaplan 2013). Apis mellifera is a long-established species of honey bee native to Europe, western Asia and Africa. Honey bees were only introduced to North America in the early 1600s and have since established a reputation as small but important indicators of ecosystem health and stability because they exert so much control over the success of both fauna and flora. This species has been thriving for the past 50 million years; there is even evidence of beekeeping in cave paintings from ancient Egyptian, Grecian, and Stone Age times

(Huston 2013).

In the past decades, however, numbers of honey bee colonies have started to dwindle and in 2006 a new phenomenon called Colony Collapse Disorder (CCD) began to sweep the nation. The multiple interacting causes of decline may include pathogens

(such as mites, viruses, or bacteria), pesticides (particularly Neonicotinoids), management stressors (like the increased use of beehive rentals), and environmental stressors (like climate change). In light of our increased demand for bee pollination, there has been a recent push for progress in characterizing the unknowns about this mysterious condition that is endangering part of our food supply.

Honey bee Life Cycle

One colony of honey bees is considered one “superorganism” and can house 40 to

50 thousand individual bees. Within one colony, there are three castes: workers, drones, and queens (Figure 4). Worker bees, or unfertile females, typically make up 99 percent of a colony. Their primary job is to forage for nectar, pollen, water, and propolis; maintain the hive; and keep the colony organized and free of diseases. Drones are male and physically bigger than worker bees; their primary job is to find and mate with the queen.

There is only one queen or fertile female per honey bee colony. Genetically, a worker bee does not differ from a queen bee. Worker bees select a young larva and feed her a white, protein-rich substance called royal jelly to turn her into a queen. She grows to produce pheromones that keep the colony together, but perhaps more importantly, she produces eggs for the colony. A healthy queen can live for five to seven years, and produce about

2000 eggs per day (Huston 2013). Honey bees undergo complete metamorphosis, or four stages of their life cycle: egg, larva, pupa, and adult. The brood is the name for first three stages. The honey bee life cycle begins once the queen deposits an egg into a clean cell.

There are different cells for each caste type. Unfertilized eggs develop into male drones while fertilized eggs turn to female workers or queens. There in the cell, the egg develops into a larva, which eventually develops into a pupa. It takes 21 days for a worker to become an adult, but the timeline differs for each caste (17 days for a queen and 24 days for a drone).

Figure 4: Worker honey bees and their queen marked in yellow (Photo by: Reid Pryzant).

Honey bee Anatomy

When considering honey bee anatomy, form makes for function. Honey bees are built to be successful pollinators. They have two sets of eyes: a set of three dense ocelli that detect light intensity and two additional compound eyes that decipher colors and the position of the sun (Figure 5). A bee’s body hairs can collect pollen, regulate body temperature, and detect UV light levels and wind speed and direction. Their antenna act as smell detectors; as a result, bees are 100 times more sensitive than humans to odors such as flowers, nectar wax, and propolis (Wilson 2007). Their proboscis, or folding tongue, allows them to reach deep into flowers for nectar (Figure 6). Their mandibles hold wax, collect propolis, help ingest pollen, and aid in cutting, cleaning, grooming, and fighting. Their feet feature hooks that allow bees to hang onto petals and pads to walk upside down on surfaces (Figure 6). Honey bees have three sets of legs: front legs to clean the antennae, middle legs to collect wax, and hind legs to hold pollen with a corbiculae or a pollen basket capable of holding eight milligrams of pollen (Wilson

2007).

Figure 5: Honey bees have many anatomical features that allow them to be effective pollinators (sonomabees.org/images/anatomyBee.gif).

Honey Bee Pollination

These anatomical features help honey bees pollinate with efficiency and endurance. Perennial honey bees keep very busy, even in the winter. Each load of pollen might be a result of 1,500 flowers and each gallon of nectar may be a product of 500 million flowers and 7 million miles (Wilson 2007). Bees pollinate 16% of the world’s flowering plant species and 400 of the world’s agricultural plants - including important grain components of feed for beef cattle and about 95 kinds of fruits and vegetables grown in the United States (Wilson-Rich 2012, Kaplan 2013). Another 15% of the food

Americans eat comes indirectly from animals that eat foods that bees pollinate (Kaplan

2013). As a result, about a third of human food is directly or indirectly supported by honey bee pollination (Kaplan 2013).

In order for a fruit to develop, the pollen produced by the male flower part

(anther) must be transferred to the female flower part (stigma). This transfer is called pollination. Once the pollen reaches the stigma, it germinates, grows into an ovary, unites with ovule, and develops into a seed. When every seed natural to the fruit is fertilized, the fruit develops perfectly. The less complete blossom fertilization, the less perfect the fruit

(Veatch 2014). Inadequate pollination can lead to incomplete seed formation and lopsided fruit (Veatch 2014). The value of bees as pollinators is further exemplified by a set of screen test experiments in Michigan (Veatch 2014). First, a McIntosh apple tree was screened to keep bees away from it. The screened tree set twenty-five apples while its nearest neighbor forty feet away (where cross-pollination was allowed) set over 1200 apples. In another experiment, one cherry tree was caged to prevent the bees from

32 reaching it. The harvest was four pounds of cherries. Another tree the same size, exposed to bees, gave a harvest of forty-four pounds of cherries. This difference in harvest yield demonstrates the value of honey bees as pollinators.

(ii) Case Study: Natural History of Bombus sp.

Reductions in managed honey bee colonies have helped to raise awareness of how much we rely on bees in general for their ecosystem services (Williams et al. 2014).

Although the plight of the honey bee spurred research on other native pollinators such as bumble bees, interspecific pollinator competition between Apis mellifera and native species is one of the main causes of native bee decline. As highly polylectic generalist feeders, Apis mellifera decrease native pollinator numbers by exploitive competition because they usurp available nectar and pollen resources in the community and encourage the co-introduction of natural enemies, pathogens, and pollination of non-native flora

(Thomson 2004, Goulson et al. 2005, NRC 2007, Goulson 2010). The niche overlap between honey and bumble bees is as high as 80 to 90 percent when resources are scarce

(Thomson 2004, NRC 2007). Although there is only a negative relationship in 1 of 7 months of observation, more bumble bee foragers as gain distance from honey bee colonies (Thomson 2004).

Bombus Life Cycle

As social insects, bumble bees are very similar to their close relatives the honey bees. However, one differing characteristic is that while honey bee colonies can persist with the same queen for years, bumble bee colonies die at the end of each growing season, with new ones founded each year (Williams et al. 2014). The bumble bee life cycle starts in the spring, when mated and overwintered queens emerge from hibernation and seek a nest site to construct a wax honeypot for nectar storage (Figure 6). She lays her first clutch of eggs in a brood clump and alternates between incubating the hatched larvae and foraging for food for two weeks. The larvae then spin a silk cocoon and pupate for another two weeks. Adult bees emerge and live as workers in the colony. As the season wears on and more floral resources become available, bumble bee colonies grow quickly (Figure 7). During summer, the colony switches to producing males and new queens. Males do not forage for the colony, but rather fledge the nest and search for mates. New queens mate once with only one male, build fat reserves, and search for a suitable overwintering site (Figure 7). While the colony declines and dies, the new queen starts another annual cycle come spring (Williams et al. 2014).

Figure 6: The yearly life cycle of the bumble bee colony occurs in four primary stages: (1) The queen emerges from hibernation and starts new colony by herself. (2) Workers (females) are produced and start to forage; the colony develops and grows. (3) Unfertilized eggs (males) are laid and worker larva develop into new queens. (4) Males and new queens mate, the colony disintegrates, the old queen, workers and males die, and new queens hibernate (http://www.bumblebee.org/images/lifecycle.jpg).

Bombus Anatomy

There are nearly 20,000 species of bees in the world, of which only 250 belong to the genus Bombus (Williams et al. 2014). In the eastern United States, whose boundaries are fully east of the 100th meridian, there are a total of 21 bombus species (Colla et al.

2011). However, there are many differences among bombus species in terms of timing of emergence, length of colony cycle, foraging behavior, and habitat selection (Colla et al.

2011). The length of the bumble bee’s tongue governs its food-plant-flower choices; bees prefer flowers with a similar depth to their tongue length, as this tends to maximize the rate that they can gather nectar and pollen resources (Colla et al. 2011). There are about seven species of bombus commonly found in the eastern United States that favor

Solidago and have a phenology that matches the time of capture for this study: Bombus impatiens, Bombus bimaculatus, Bombus ternarius, Bombus terricola, Bombus affinis,

Bombus griseocollis, and Bombus pensylvanicus.

The most distinctive characteristics to consider while identifying bombus to the species level are the sex of the specimen, the angle of the distal posterior corner of the midleg basitarsus, the dimensions of the cheek or oculo-malar space, and the general color patterns of the head, thorax, and terga. To determine the sex of the specimen, consider the following morphological characters: females have 6 segments (terga) visible dorsally on the metasoma (7 segments for males); the tip of their abdomen is usually more pointed; their antennae are short with 12 segments (rather than longer with 13 segments); and their mandibles do not have a beard of dense long hairs (Williams et al.

2014). Next, determine whether the angle of the distal posterior corner of the midleg

36 basitarsus is less or more than 45 degrees. Then, determine if the cheek is longer or shorter (or equal) than it is broad. Lastly, consider the specific hand and microscopic characters of each possible bombus species to make the final species determination.

Bombus Pollination

Bumble bees make particularly effective pollinators because of their distinct foraging behaviors. For instance, in contrast to honey bees, bumble bees exhibit high directionality in their foraging behavior; after visiting a head, a bumble bee returns to one central hive to deposit or consume the harvest of pollen and nectar before going out again to the next head (Mani and Saravanan 1999). Bumble bees generally avoid revisiting a flower but tend to forage in a relatively wide geographic and temporal range (Mani and

Saravanan 1999). As generalists, they can collect resources from a patchy environment and forage in harsher climatic conditions than other bees; for instance, unlike honey bees, they are able to forage under cold, rainy, and cloudy conditions (Colla et al. 2011).

Bombus foraging times are not rigidly fixed (Mani and Saravanan 1999), but they tend towards longer hours of foraging -even at night- with a relatively long period of summer activity (Colla et al. 2011, Williams et al. 2014). Bumble bees have a foraging radius of

8km (Mani and Saravanan 1999) but have been demonstrated to fly up to 10 km away from their nest (Williams et al. 2014). We see that bees fly greater distances and make more visits to flowers than do other taxa because any one bee is collecting not just for her own needs but for the offspring as well (Willmer 2011). In addition, bumble bees can

37 vibrate their flight muscles and engage in “buzz” pollination to more effectively extract pollen from anthers and improve crop yields (Williams et al. 2014).

Native bumble bees are vital as pollinators for a number of various food crops such as peppers, cucumbers, tomatoes, blueberries, cranberries, eggplants (Williams et al.

2014). Their pollination services represent more than $10 billion annually (Velthuis and van Doorn 2006). Unfortunately, bumble bee populations and species distributions have been in decline since the 1950s (Free and Butler 1968, Kosior et al. 2007, Grixti et al.

2009). Quantitative evidence indicates over 90% range decline in Great Britain and

Ireland and over 80% in the United States (Cameron et al. 2011, Meeus et al. 2011). In

Naturalists in North America have been describing bumble bee diversity for more than two centuries, but a great deal remains to be done to characterize these pollinators of wild and cultivated plants (Williams et al. 2014).

METHODS

Study Site

We studies fall blooming flowers and their pollinators in the weather monitoring station field of Hopkins Memorial Forest (HMF) in Williamstown, Berkshire County,

Massachusetts, USA. HMF is a 2600-acre (1040-ha) reserve that spans Massachusetts,

New York, and Vermont states. The grounds are managed by the Williams College

Center for Environmental Studies (CES) with the goal of facilitating research and undergraduate teaching activities while preserving and monitoring forest resources though long-term ecological research. The site contains an open field habitat surrounded by temperate deciduous forest typical to New England. This field was selected as our experimental study site because open area habitats surrounded by woodlands provide a particularly good chance for collecting bees and seeing a variety of species (Williams et al. 2014). In addition, the onsite weather station could provide meteorological data and a detailed site history is available to us.

Site History

In the early 1900s, local farmers used the weather field area as pasture. From

1930 to 1950, the area lay fallow until the U.S. Forest Service started growing tree plantations (Populus sp., commonly known as aspen, and Acer sp., commonly known as maple) in 1950 to 1952. A project to expand the weather station field began in 2003: the ground was graded and trees removed from half an acre of land. The next summer, three additional acres were cleared (3.5 total now) and seeded with an annual cover crop of

39 buckwheat. In 2005, all 3.5 acres were disc-harrowed and seeded with cold-season perennial grasses. In the summer of 2010, the field was surveyed into twelve 24m x 24m plots in accordance with the research proposed by Professor Joan Edwards. In the summer of 2011, the survey was expanded to sixteen plots, up from the previous twelve; the baseline vegetation surveys began; and the field was mowed for the last time before the first series of mowing treatments started in 2013. Prior to the first treatments in 2013,

Professor David Smith took photographic baseline surveys at the center of each plot. Four annual mowing plots were mowed in July and four were mowed in October of 2013. The following year in 2014, the second series of treatments (including all plots this time- both annual and biennial) were implemented. Students in the fall 2013 Williams College

Ecology class conducted preliminary surveys of all plots. The survey data used in this study are from September and October 2014 (Figure 7).

Figure 7: Professor Joan Edwards and Julie Jung in the process of surveying Plot A3 in the weather station field in HMF in September of 2014.

Treatments & Experimental Procedure

To test my hypotheses, that early mowing and mowing every year will produce less floral resources and pollinator abundance, I considered two factors: mowing time and mowing frequency. The two treatments for mowing time were early (end of July) and late

(beginning of October). The treatments for mowing frequency were every year and every other year, with treatments that began in the summer of 2013. We set up a full factorial randomized block design with two factors yielding four possible treatments: Early

Annual (EA), Early Biennial (EB), Late Annual (LA), and Late Biennial (LB). These four treatments were randomly assigned to blocks of four groups of plots. Plots were grouped by similarity in location and vegetation for a total of 16 plots divided into four blocks: A, B, C, and D (Figure 8). Each plot is 24m x 24m and is divided into 144 2m x

2m quadrats. The A1-A4 plots are located on the southeast edge of the field; the B1-B4 plots at the top of the field; the C1-C4 plots along the southwest edge of the field; and the

D1-D4 plots in the center of the field (Figure 8).

Figure 8: Map of all 16 24m x 24m experimental plots of the Hopkins Memorial Forest Weather Station Field with the four blocks indicated by A, B, C, and D. Plots and treatments were assigned based on a full factorial randomized block design with two factors yielding four possible treatments: Early Annual (EA), Early Biennial (EB), Late Annual (LA), and Late Biennial (LB). Each plot is labeled based on mowing treatment and block.

Spatial Distribution Study

Hypothesis 1: Late mowing and mowing on alternate years increase floral yield and diversity. We manually counted and identified flowering stems in each of the sixteen study plots. Field surveys took place from late September to mid October. We sampled the inner 6 x 6 quadrats (12m x 12m) to allow feasibility and account for inconsistencies in mowing along the edges (Figure 9). We took each 2m x 2m quadrat and recorded the x- and y- coordinates to produce maps of all the flowering stems. Each student also recorded the height of tallest plant in each plot, in case those data might hint at some difference between plots treated with different mowing frequencies. After mapping the spatial distributions of individual flowering stems for each species in each plot, we determined the density and diversity of blooming stems in mowed vs. unmowed treatments and measured the size of clumping for the four most common species:

Solidago gigantea, S. altissima, S. rugosa, and Euthamia graminifolia. For the clumping analysis, the area of each clump was calculated by species for every plot. These areas for each treatment type were compared using notched boxplots with 95% confidence intervals and standard deviation (R_Core_Team 2014).

Figure 9: A more detailed diagram of a single large plot describing the sampling method. The area outlined in blue is the inner 12 x 12 m of each plot, where all flowering stems were counted and mapped by dividing each 2 x 2 m plot into smaller ¼ m2 quadrats (shown on the right).

Pollinator Activity Study

Hypothesis 2: Late mowing and mowing on alternate years increase pollinator abundance and diversity. We simultaneously filmed stems of Solidago rugosa at all four plots in a block. We used Brinno 200 Pro HDR time-lapse cameras housed in weather resistant cases (Brinno, Taipei City, Taiwan) (Figure 10). The cameras captured an image every three seconds and produced a timelapse video that is date and time stamped. We selected and recorded stems on mowed and unmowed plots simultaneously to determine how each treatment affects pollination visitation (Figure 10). We chose S. rugosa for this analysis because it was the only goldenrod species blooming on all of the plots. To select stems in each plot, we randomly selected coordinates and chose the closest flowering stem to that point.

Figure 10: Camera set up for pollinator activity study.

This timelapse video method is efficient because it provides accurate, detailed, and unbiased (no observation bias) records of plant-pollinator interactions and allows for simultaneous observations at multiples sites. Direct observation of visitors in situ, in contrast, can be biased, flawed, and incomplete because the presence of people can alter visits by insect and when many visitors land on flowers they are difficult to track and record (Edwards In press). The cameras can take pictures up to 1280 x 720 resolution and be customized to snap shots at different time intervals from 1 per second to 1 per 24 hours. We set them to take one picture every three seconds. The manual focus allowed for various focal lengths to the subject. For our purposes, we set the cameras to a focal length of six inches. Moreover, they are inexpensive, portable, waterproof, and easy to use in the field. They produce time-stamped video product that can be analyzed on a frame-by-frame basis, allowing us to answer a wide range of ecological questions.

We filmed each of the four designated stems in each of the 16 plots for three days, totaling over 4500 hours of S. rugosa video from September 7, 2015 to September 19,

2015. We analyzed and scored one 24-hour interval for each of the four stems in each of the 16 plots, totaling over 1500 hours of scored footage. We scored the same 24-hour period for each block. To remain consistent while scoring videos, visits counted if there was any contact with the reproductive structures of the goldenrod and a specimen flew out of the frame and one of the same species flew back in the frame and onto the specified area, those counted as two separate visits. In selecting samples, we chose about

40 capitulescences to score. If there were no inflorescences with 40 capitulescences, we

46 chose the largest available one. In two cases, Plot D1 point B (D1ptB) and Plot D1 point

C (D1ptC), both had approximately ten and twelve capitulescences respectfully.

I also collected pollinators found on goldenrods in the field throughout the field season to provide voucher specimens and familiarize myself with the species that visit these flowers and how they behave. These voucher specimens were captured with an insect net, transferred directly to petri dishes, killed by freezing, and later examined for pollen and pinned. These collections (and additional local collections from theses past) helped to identify many of the pollinator visitors from the video recordings of S. rugosa stems. The images of the insect visitors to S. rugosa were clear enough most of the time for some taxonomic identification- to species, genus, or family level. We were conservative in our identifications and categories, only naming species, genera, family, or order if certain of its rightful classification (Appendix 2).

RESULTS

Part A: Flowering Stems

(a) Abundance by Treatment

Late mown plots had significantly more flowering stems than early mown plots

(One-way ANOVA, F (1,14) = 9.416, p = 0.00834) (Figure 11). ANOVA tested five models to partition out treatment time and/or frequency effects on the number of flowering stems on each plot: number of stems (N) as a function of 1, N as a function of time, N as a function of frequency, N as a function of time and frequency, and N as a function of time by frequency. AIC values were 270.2, 266.6, 272.2, 268.6, and 270.6, respectively. As tested by AIC (Akaike Information Criterion), the best-fit model was N as a function of treatment time, compared to time and frequency, one, time by frequency, and frequency (ΔAIC = -2, -3.6, -4, and -5.6 respectively). The treatment frequency

(whether the plots were mowed annually or biennially) did not have a significant impact on the number of later counted stems (One-way ANOVA, F (1,14) = 0.215, p = 0.65)

(Figure 12). Since there are a significant number of outside values from our figure of flowering stems by treatment, frequency distributions of flowering stems per quadrat may be more informative (Figure 12).

Figure 11: The log of the number of stems in each treatment shows significantly more flowering stems in late mow treatments.

Note (re: notched boxplots)

The bold horizontal bar indicates the median; the boxes show the upper and lower quartiles; the arrows show the minimum and maximum observations; and the circles show outliers. If two boxes' notches (where the box starts to taper) do not overlap, there is ‘strong evidence’ their medians significantly differ with approximately 95 percent confidence (Chambers et al. 1983).

Figure 12: Frequency distributions of the log of the number of flowering stems per (4m2) quadrat show significantly more stems per quadrat in late mow plots. Early mown plots also have significantly more plots with no flower stems (note differences in scale). Moreover, since there are many empty quadrats in our sample, the overwhelming influence of the first column in each frequency distribution plots makes it difficult to assess the distribution of the stems per quadrat frequencies (Figure 12). When the data are limited to only include quadrats with greater than one stem, the frequency distributions of the log of the number of flowering stems per (4m2) quadrat clearly show significantly more stems per quadrat in late mow plots (Figure 13).

Figure 13: When data are limited to only include quadrats with greater than one stem, the frequency distributions of the log of the number of flowering stems per (4m2) quadrat clearly show significantly more stems per quadrat in late mow plots.

(b) Spatial Distribution Maps of Each Plot

Spatial distribution maps help to visualize the distinct growth and clumping patterns in each of the 16 plots. These allow us to visually observe and assess the influence of mowing treatments on the density, diversity, richness, or evenness of stems in each plot (Appendix A). For instance, we can observe that dispersion patterns for the four Early Annual mowed plots (A1, B2, C4, D1) indicate minimal growth of all species with S. rugosa most abundant (Figure 14). Dispersion patterns for the Early Biennial mowed plots (A2, B1, C2, D3) show low signs of growth and widely spaced species distributions (Figure 15). Dispersion patterns for the Late Annual mowed plots (A4, B4,

C1, D2) show much greater growth and more clumping amongst species (Figure 16).

Dispersion patterns for the Late Biennial mowed plots (A3, B3, C3, D4) show a significantly greater number of stems throughout the majority of the plots and very close clumping (Figure 17).

Figure 14: Dispersion patterns for the four Early Annual mowed plots (A1, B2, C4, D1) indicate minimal growth of all species species with S. rugosa most abundant. Each dot represents one flowering stem, color-coded by species.

Color Key: = E. gramifolia = S. gigantea = S. rugosa = S. altissima

Figure 15: Dispersion patterns for the four Early Biennial mowed plots (A2, B1, C2, D3) show low signs of growth and widely spaced species distributions. Each dot represents one flowering stem, color-coded by species.

Figure 16: Dispersion patterns for the Late Annual mowed plots (A4, B4, C1, D2) show much greater growth and more clumping amongst species. Each dot represents one flowering stem, color-coded by species.

Figure 17: Dispersion patterns for the Late Biennial mowed plots (A3, B3, C3, D4) show a significantly greater number of stems throughout the majority of the plots and very close clumping. Each dot represents one flowering stem, color-coded by species.

Flowering stems formed significantly larger patches in late mowing treatments compared to plots with treated with early mowing (One-way ANOVA, F (1, 270) = 42.85, p

= 2.97e-10) (Figure 18). These patches or clusters in goldenrod growth can be visualized in spatial distribution maps of each plot (Figures 14 to 17). This clumping in goldenrods is likely a result of their clonal growth pattern. Mean patch sizes ± 95% confidence intervals are much greater for late annual (0.62 m2 ± 0.34 m2) and late biennial (0.40 m2 ±

0.20 m2) plots, compared to early annual (2.43 m2 ± 0.72 m2) and early biennial plots

(2.83 m2 ± 0.93 m2) (Figure 18).

Figure 18: The log of patch size by plots according to treatment type show significant difference between early biennial and both late annual and late biennial plots.

This pattern holds when comparing clump sizes in individual plots (Cohen 2014).

In A block plots, the clumping area of plot A1 (early annual mow) is smaller than clumping areas in plots A3 (late annual mow) and A4 (late biennial mow) (Figure 19). In the B block, there are significant increases in clumping area between B1 (early annual) and B2 (early biennial). B1 also has significantly lower clumping area compared to plots

B3 (late annual) and B4 (late biennial) (Figure 19). In the C block, plot C2 (early biennial) has smaller clumping size compared to C1 (late annual), C3 (late annual) and

C4 (early annual), however sample sizes for C2 and C3 are small (Figure 19). D3 (late annual) has a lower clumping area than D4 (late biennial), but D block plots also have a very low sample index (Figure 19).

Figure 19: The log of patch size (m2) for each research plot indicates significantly larger patches for late mown plots. Observing clumping area for individual species across all plots, there is only a significant difference between Solidago gigantea and Solidago rugosa (Figure 20).

Analyzing clumping area by species for each treatment type, the area of Euthamia graminifolia clumps increase between early annual plots and late annual as well as late biennial plots (Figure 21). For Solidago altissima, there are no significant differences

(Figure 21). Solidago gigantea clumps have marked increase in area between early annual and early biennial plots (Figure 21); there is also a large increase between early annual and late annual, as well as late biennial plots (Figure 21). Significant increase in clumping area between early biennial plots compared to late annual and biennial plots is observed for Solidago rugosa (Figure 21).

Figure 20: The log of patch size (m2) for all four species show that patch size area does not vary significantly for three species (Solidago gigantea, Solidago altissima, and Solidago rugosa) by species type. The only significant difference is between Solidago gigantea and Solidago rugosa.

Figure 21: Clumping area (m2) for each species by plot treatment shows significant differences in early and late mowing for Euthamia graminifolia (Egram), Solidago gigantea (Sgiga), Solidago altissima (Salt) and Solidago rugosa (Srug). For each individual species, patch size is larger in late mown than early mown plots.

(d) Height of the Tallest Plant in Each Quadrat

The heights of the tallest plant in each quadrat of late mow plots were significantly taller than the height of the tallest plant in each quadrat of early mow plots

(One-way ANOVA, F (1, 14) = 65.71, p = 1.17e-6) (Figure 22). The frequency of mowing treatments, however, did not significantly affect plant height (One-way ANOVA, F (1, 14)

= 0.087, p = 0.772).

Figure 22: The tallest plant height in each quadrat grouped according to treatment type shows significant differences between early and late mowed plots.

(e) Floral Diversity, Richness, and Evenness

Standard diversity indices include the Shannon-Weiner Diversity Index (H’), evenness (J), and family richness (S). Shannon-Wiener Diversity Indices (H’) grouped according to treatment type show no significant differences between or among treatments

(Two-way ANOVA (time*frequency), d.f. = 1, F = 4.493, p = 0.0556) (Figure 23).

Evenness Indices (J) grouped according to treatment type also show no significant differences between or among treatments (One-way ANOVA, d.f. = 1, F = 2.336, p =

0.152) (Figure 24). Richness (S) grouped according to treatment type show no significant differences between or among treatments (Two-way ANOVA (time*frequency), d.f. = 1,

F = 4.085, p = 0.0661) (Figure 25). In order to quantify the standard diversity indices and rarefaction curve of flowering stems on each plot, we used the Vegan package in R

(R_Core_Team 2014).

Figure 23: Shannon-Wiener Diversity Indices grouped according to treatment type show no significant differences between or among treatments.

Figure 24: Evenness Indices (J) grouped according to treatment type show no significant differences between or among treatments.

Figure 25: Richness (S) grouped according to treatment type show no significant differences between or among treatments.

(f) Rank Abundance Analysis/Dominance Diversity Curve Dominance diversity curves show that there are different dominant species depending on treatment time (Figure 26). For instance, Solidago gigantea dominate the late mown plots whereas S. rugosa dominated Early Annual and S. lateriflorum dominated the Early Biennial (Figure 26). Also called rank abundance analysis, these figures depict the number of flowering stems per treatment and per species of asteraceae, ranked from most to least common (Figure 26).

Figure 26: Dominance diversity curves show that Solidago gigantea dominate the late mown plots whereas S. rugosa dominated Early Annual and S. lateriflorum dominated the Early Biennial

Part B: Pollinator Visitors

(a) Abundance by Treatment

Both early mow treatments had significantly fewer visitors than late mowing treatments, thus the time of mowing (early or late) has a significant effect on pollinator visitation rates (One-way ANOVA, F (1, 270) = 42.85, p = 2.97e-10) (Figure 29).

Figure 29: Both early mow treatments had significantly fewer visitors than late mowing treatments.

(b) Pollinator Diversity, Richness, and Evenness

In order to quantify the standard diversity indices and rarefaction curve for pollinator visitors to each plot, we used the Vegan package in R (R_Core_Team 2014).

Standard diversity indices include the Shannon-Weiner Diversity Index (H’), evenness

(J), and family richness (S). Notched boxplots of Shannon-Wiener Diversity Indices (H’) grouped according to treatment type show that early mow plots host significantly lower diversity levels than late mow plots (Two-way ANOVA (time * frequency), F (1, 12) =

11.880, p = 0.00483) (Figure 30). Notched boxplots of Evenness Indices (J) grouped according to treatment type also show that the interaction term time * frequency is significant (Two-way ANOVA (time * frequency), F (1, 12) = 6.279, p = 0.0276) (Figure

31). Notched boxplots of Richness (S) grouped according to treatment type show that early mow plots host significantly lower richness levels than late mow plots (Two-way

ANOVA (time + frequency), F (1, 13) = 17.417, p = 0.00109) (Figure 32).

Figure 30: Notched boxplots of Shannon-Wiener Diversity Indices grouped according to treatment type show that early mow plots host significantly lower diversity levels than late mow plots (Two-way ANOVA (time * frequency), d.f. = 1, F = 11.880, p = 0.00483).

Figure 31: Notched boxplots of Evenness Indices (J) grouped according to treatment type also show that the interaction term time * frequency is significant (Two-way ANOVA (time * frequency), d.f. = 1, F = 6.279, p = 0.0276).

Figure 32: Notched boxplots of Richness (S) grouped according to treatment type show that early mow plots host significantly lower richness levels than late mow plots (Two- way ANOVA (time + frequency), d.f. = 1, F = 17.417, p = 0.00109).

A rank abundance curve or Whittaker plot is a chart used to display relative species abundance, a component of biodiversity. The x-axis displays the abundance rank: the most abundant species is given rank 1; the second most abundant is 2; and so on. The y-axis displays relative abundance, usually on a logarithmic scale. This is a measure of species abundance (for example, the number of individuals) relative to the abundance of the other species. A rank abundance curve also can be used as a tool to visualize other diversity statistics such as species richness and evenness. The number of different species on the chart or the number of species ranked is a measure of species richness. You can also derive species evenness from the slope of the line that fits the graph. A steep gradient indicates low evenness, as the highest ranking species often have much higher abundances than the low ranking species; while the a shallow gradient in turn indicates high evenness, as the abundances of different species become more similar.

From our data, we can see that the numbers of species ranked in late treatment plots (33 and 30 species in LA and LB treatments, respectively) are greater than the number of species ranked in early treatment plots (17 and 23 species in EA and EB treatments, respectively). Thus, later mowing can increase species richness (Figure 33).

This result is consistent with our previous boxplot analysis of richness (Figure 32). Also consistent with previous analysis of evenness indices, the gradients of our rank abundance curves do not suggest that the timing of treatments lead to corresponding changes in evenness (Figures 31 and 33).

Figure 33: Rank abundance plots display abundance rank on the x-axis and relative abundance on the y-axis. Combined, these data on species abundance relative to the abundance of other species suggest that the timing of treatments has a significant impact on biodiversity- namely that later treatments increase biodiversity indices.

(d) Rarefaction Curve

The number of novel species detected as a function of the number of stems sampled shows that the number of species has not yet reached a leveling point. This rarefaction analysis as shown by species accumulation lines indicates that we have not yet captured most visitors in the plots.

Figure 34: The rarefaction curve for pollinators shows the number of novel species detected as a function of the number of pollinator visitors recorded. This rarefaction analysis suggests that the number of species, depicted by species accumulation lines, has not yet reached a leveling point.

DISCUSSION

Flowering Stems

Later mowing increases floral resources for pollinators, which we measured in terms of number of flowering stems, level of clonal growth, and tallest plant height.

Abundance analyses of the number of flowering stems in each plot by treatment indicate that plots with late mow treatments grew to have significantly more flowering stems/quadrat compared to plots with early mow treatments (Figures 12 to 14). The frequency of treatment (if plots were mowed annually or biennially) did not have a significant impact on the number of flowering stems in each plot. A greater number of stems indicate greater floral resources for pollinators of spring-blooming Asteraceae. The height of the tallest plant in each quadrat is an additional indicator of plant growth. Plant heights grouped according to treatment type show significant differences between early late mowed plots (Figure 22). The last indicator of plant growth that we considered is degree of clonal clumping. Analyzing all plots of a given treatment together, there are significantly smaller clumping areas in plots treated with early mowing compared to plots with late mowing treatments (Figure 18). We used spatial distribution plots to easily visualize the number of flowering stems and clumping pattern in each plot, grouped by treatment.

These distribution plots also allowed us to visualize relationships between individuals of certain populations or species, as well as the relationship between a population or species and its environment. Spatial patterns can be categorized into three basic types: random, even, and clumped or aggregated. A randomly distributed plot

76 means that individual stems are arranged in an unpredictable pattern, that all areas of a certain habitat are equally likely to be inhabited, and that the position of one individual has no effect on the position of another at a neighboring site. An even distribution occurs when individuals are spaced at equal distances from one another. A clumped distribution includes some areas with dense inhabitation and other areas with very little habitation.

These distribution patterns reflect underlying conditions, such as environmental heterogeneity, a plant’s seed dispersal mechanism, or other species-specific traits (Cohen

2014).

The area of clumping increases between plots with early and late mowing patterns, likely due to goldenrod clonal growth. Colonies of plants expand as times passes, therefore increasing the clumping size. There is, however, no significant difference in clumping area between annually and biennially mowed plots. The most significant increase in clumping area is between early biennial and late biennial plots, rather than between early annual and late biennial plots, as expected. This could mean that goldenrods grow most significantly in the time between early and late mows. Perhaps a die-off between years, during the winter season, explains the decline between early annual and early biennial clumping areas (Cohen 2014). This decrease may not exist in late-mow plots because the plants would have time to regenerate during the next growing season (Cohen 2014).

Comparing trends in clumping area by species for each treatment indicates the same trend as clumping patterns for all species combined: an increase in area of clumps between plots with early and late mows (Figures 20 and 21). Important because larger

77 clumps are an indicator of plant growth and pollinators may go to larger patches more.

The lack of significant difference in clumping area between Solidago species indicates that clumping size is not determined based on species type, but rather is standardized for all goldenrods. Since increases in clumping in goldenrod do not depend on species type, the likely cause of increased clumping area in later mow treated plots is the clonal growth pattern of goldenrods (Cohen 2014).

In our analysis of the standard diversity indices applied to different mowing treatments, our results differed from our hypothesis. Rather than decreasing in early mow treatments, diversity, evenness, and richness showed no significant differences between or among treatments (Figures 30 to 32). This deviation from expected might be a result of block differences between sets of plots. Recall that in 2005 the whole field, including the experimental space, was seeded with cold-season perennial grasses. In anticipation of this study, we wanted to start at ground 0 with a field of similar and homogenous plots. The general soil type in the field was amenia silt loam, a relatively heavy, sily, and wet soil type. Observations and crude soil samples indicate that there is poor drainage and moist soil conditions in the middle of the field, where the D-plots are located (Andrew Jones, personal communication). Rushes and sedges, wetland indicators, dominate as the tallest plant species found per quadrat in these plots. These dominant wetland species may contribute to lower diversity, evenness, and richness values within goldenrod and aster communities.

A possible next step if we were to continue this analysis might be to check the photographs of plots just before the mowing treatments to see if some plots had a

“headstart” in terms of goldenrod abundance and diversity. For instance, since our observations indicate that Plot D4, an early biennial plot, is a wet plot with many rushes and sedges, it might be helpful to ask if other D-plots (treated differently) had higher abundance or diversity of goldenrod than D4 to begin with. If we quantify some baseline level of growth and/or diversity before the treatments began, we can quantify and account for those differences in our analysis.

Results from rank abundance analysis plotting the number of flowering stems per treatment and per species of asteraceae, ranked from most to least common, show that different species dominate the plot in abundance depending on treatment time (Figure

26). First, it is important to notice the different scale on the Y-axis in early and late treatment plots. As previously shown, there are many more flowering stems in late treatment plots than in early treatment plots. In addition, Solidago gigantea dominate the late treatment plots to a greater degree than in early treatment plots (Figure 26). These differences indicate that if we had done pollinator analysis on S. gigantea rather than

Solidago rugosa, we may have found even greater differences between early and late mow treatments.

Pollinator Visitors

Now that we have examined the influence of mowing schedules on abundance and diversity of flowers, and we will focus on the impact of mowing on pollinators of

Solidago rugosa. Our results indicate that early mowing decreases pollinator abundance and diversity. As expected, flowering stems in late mown plots attracted more insects

(One-way ANOVA, F (1, 270) = 42.85, p = 2.97e-10), more insect species (Two-way

ANOVA (time + frequency), F (1, 13) = 17.417, p = 0.00109), more evenness (Two-way

ANOVA (time * frequency), F (1, 12) = 6.279, p = 0.0276), and a higher diversity of insects (Two-way ANOVA (time * frequency), F (1, 12) = 11.880, p = 0.00483).

Rank abundance curves of pollinator visitors show increased numbers of insects

(height of bars) and taxa (number of variables on the x-axis) in late mown plots (Figure

33). The longer tail on the rank abundance curves of late plots relative to early plots suggests increased species richness (number of taxa on the x-axis) and that the goldenrods are drawing from a larger variety of species. These are indicators of a more resilient pollinator system in late mown plots (Figure 33). Moreover, Bombus impatiens was the most abundant visitor to our plots and showed significant differences among all treatments (Figure 33). Since Bombus is a genus that is declining worldwide, the large influence of treatment effect suggests that late and less frequent mowing is an important tool for helping to prevent pollinator decline (Cameron et al. 2011).

CONCLUSION

The work of a previous thesis student suggests that changes in current mowing patterns can potentially increase floral production as well as support a more diverse and productive pollinator population (Hancock 2011). She conducted a local survey of over

1000 acres of open land and determined that caretakers and property owners of over 70 percent of the lands without committed mowing schedules in Williamstown, MA (180 out of 253.5 acres) indicated they were willing to delay mowing until October or later to promote the growth of goldenrod and pollinator populations, suggesting that delayed mowing is both effective and feasible (Hancock 2011). Since land management practices and local habitat conditions can help bolster pollinators (Williams and Kremen 2007), there is immense potential to increase the success of such species in Williamstown and broader regions. My full factorial randomized plot design quantifies the impact of frequency of mowing and time of mowing and provides clear evidence for land management practices that will manage flower production and pollinator diversity.

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APPENDICES

Appendix 1: Spatial Distribution Maps for All 16 Plots

A1 (Early Annual)

A2 (Early Biennial)

A3 (Late Biennial)

A4 (Late Annual)

B1 (Early Biennial)

B2 (Early Annual)

B3 (Late Biennial)

B4 (Late Annual)

C1 (Late Annual)

C2 (Early Biennial)

C3 (Late Biennial)

C4 (Early Annual)

D1 (Early Annual)

D2 (Late EY)

D3 (Early Biennial)

D4 (Late Biennial)

A1 (Early Annual) A2 (Early Biennial)

A3 (Late Biennial) A4 (Late Annual)

Block B: B1, B2, B3, B4

Block C: C1, C2, C3, C4

Block D: D1, D2, D3, D4

Appendix 2: Pollinator Key

Part A: Family Apidae

1. Bombus impatiens Common name: common eastern bumblebee

Physical characteristics:

Coat: paler/greyer yellow than other Bombus species Pale T1 (tergum 1) and all black T2 Shaggy haircoat Scutum: vague interalar band formed by dark interspersed hairs (compared to bimaculatus) Malar space: shorter than in bimaculatus, longer than in griseocolis Wimpy wings Medium tongue length

Season: March to November. In northeastern US, remains abundant later in fall after other species have declined in numbers and can still be found on flowers well into November!

Range: Eastern North America

http://en.wikipedia.org/wiki/File:Bombus_impatiens_distribution.svg

2. Bombus bimaculatus Common name: two-spotted bumblebee

Physical characteristics: Hue of yellow is lemonier than in impatiens or griseocollis Abdomen has two yellow spots (resembles a yellow W on back) In females, yellow of T2 extends in a deep “U” nearly to the apex of the segment medially, but not laterally because the lateral portions of T2 are largely red Female scutum has a conspicuous circular black spot Long tongue

Season: March to September

Range: Common in eastern North America

3. Bombus griseocollis Common name: common eastern bumblebee

Physical characteristics: Short, dense coat All black hair on head Short malar space Yellowish-brown T2 forms a shallow “U” - which does not extend far posteriorly in the middle - but is relatively more extensive laterally - appears more as a transverse band than as a medial strip of yellow

4. Bombus ternarius Common name: tri-colored bumble bee

Physical characteristics: Distinctive abdominal color pattern 1. one band of yellow 2. two orange-red 3. another yellow 4. two bands of black

5. Xylocopa sp. (perhaps Xylocopa virginica) Common name: eastern or common carpenter bee

Physical characteristics: Conspicuous black, round spot surrounded by yellow hairs atop the thorax Yellow pile on thorax Females have a broader head, while males have yellow/white face Absence of pubescence on the dorsum of the glossy black abdomen Lack a malar space.

Season: March to October. Early spring, late fall in temperate areas

Range: Common in eastern North America, the only member of its genus in much of its range.

6. Apis mellifera Common name: western or European honey bee

Part B: Family Halictidae

1. Augochorella/Augochlora sp. (perhaps Augochlora pura) Common name: sweat bee

Physical characteristics: Small, solitary bees roughly ½ inch in length Bright, metallic, green all over, black antennae

Range: throughout the US but concentrated in the eastern half

Season: flight season lasts from mid April to early September

2. Specodes sp. Common name: cuckoo bee

Physical characteristics: Shiny brown to black, often have red on all or part of abdomen. Slender bees with few hairs (collect nectar, not pollen) Solitary cleptoparasitic bee 0.2 to 0.6 inches (4.5 to 15 mm) long

Range: found on all continents and widespread throughout its range 80 species in North America

Season: March to September

100

Part C: Family Vespidae

1. Polistes sp. (perhaps Polistes fuscatus) Common name: golden, northern, common paper wasp

Physical characteristics: 15 to 21 mm Unusually variable color patterns, dependent on geographic location and season 3 color pattern trends that represent different regions throughout the country Male has darkened apical flagellomeres with darkened dorsal surfaces Northern females have blackened bodies, sometimes with markings Slender bodies with a waist

Range: Southern Canada to the US to Central America.

101

2. Ancistrocerus spp. Common name: genus of potter wasps, mason wasps

Physical characteristics: Nonpetiolate eumenine (subfamily Eumeninae) wasps Transverse ridge at the bending summit of the first metasomal tergum Low and opaque propodeal lamella completely fused to submarginal carina Back-curved last segments of antennae in males

102

3. Vespidae 1

Physical characteristics: Small, skinny body with small and skinny stripes.

103

Part D: Unknown Wasps

1. Wasp 1

Physical characteristics: Abdominal segments are constricted at the margins Female faces are modified with unusual projections on the clypeus Relatively even yellow stripes across abdomen

104

2. Wasp 2

105

3. Wasp 3

106

Part E: Family Syrphidae

1. Spilomyia sp. (eg. Spilomyia interrupta) Common names of syrphid flies: hoverflies, flower flies

Physical characteristics: Wasp mimic Pattern of pigment on eyes hides “fly eyes” V-mark on thorax Short antennae Spurious (extra) wing vein Aquatic in early life stages

Range: Throughout North America and worldwide

107

2. Eristalis 1

3. Eristalis 2

4. Syrphidae 1

5. Syrphidae 2

108

6. Syrphidae 3

109

7. Syrphidae 4

8. Syrphidae 5

110

Part F: Family Tachnidae

1. Tachnidae 1

Physical Characteristics: Large, bristly, and beelike or wasplike in appearance Subscutellum present Bristle pattern, facial conformation, and antennal shapes ID genera

Range: Throughout North America and worldwide

111

Part G: Family Hesperiidae

1. Hesperiidae 1 (perhaps Epargyreus clarus)

Common name: silver-spotted skipper

Physical characteristics: Adult wingspan is 43-67 mm. Mostly a mottled brown color Translucent gold spots on forewings Silvery bands on hindwings Antennae are slightly bent or curved at the tips

Range: Common everywhere in eastern US, found in a diverse variety of habitats

Season: Warm season, adults seen in daytime or sunshine.

112

Part H: Family Pieridae

1. Pieris sp. (e.g. Pieris rapae) Common name: Cabbage White Butterfly

Physical characteristics: White wings with charcoal gray tips. Males have one black spot on the center of each forewing, while females have 2 spots in the same place. Color under the forewings may be yellow or light green Color is visible if the wings are closed.

Season: Abundant species flies from early spring to late autumn. Found in fields, meadows, parks, and gardens.

113

Part I: Family Membracidae

1. Membracidae 1 (e.g. Stictocephala bisonia) Common name: buffalo treehopper

Physical characteristics: Bright green color Triangular shape 6-8 mm Transparent wings Feed off goldenrod

114

Part J: Family Tettigoniidae

1. Katydid 1

115

Part K: Family Choccinellidae

1. Hyperaspis sp. (e.g. Hyperaspis signata) Common name: signate lady beetle

Physical characteristics: Adult size 4mm to 5 mm (0.16in to 0.20in) Black with 2 red spots Smooth oval shape Lack a flared “rim” and white markings on the pronotum Underside is brown or black

Range: MA to FL, west to WI and eastern TX

Part L: Unknown Beetles

1. Beetle 1

2. Beetle 2

116

3. Beetle 3

117

Part M: Family Formicidae

1. Formicidae 1

Part N: Unknown Spider

1. Spider 1

118

Part O: Unknowns

1. Unknown 1

2. Unknown 2

119

3. Unknown 3

4. Unknown 4

120

5. Unknown 5

6. Unknown 6

7. Unknown 7

121

8. Unknown 8

122

9. Unknown 9

123

10. Unknown 10

11. Unknown 11

12. Unknown 12

13. Unknown 13

124

14. Unknown 14

15. Unknown 15

125

Appendix 3: R Scripts

Part A: FloweringStemsbyTreatment.R

# Flowering Stems by Treatment R analysis - Undergraduate Thesis, 2015 # # 22 April 2015 # # INITIALIZE WORKING DIRECTORY AND CLEAR OUT OLD DATA # set the working directory, and check that working directory is correct rm(list=ls()) ls() setwd('/Users/juliejung/Desktop/SR YR/Thesis') getwd() # check this to see that you have the correct directory!

# READ THE DATA INTO A DATA FRAME AND CHECK THE DATA # First Read in and extract the desired data ##########################

FloweringStems.df<-read.csv("FloweringStems.csv") # read the data into a data frame FloweringStems.df # type out the data frame contents

# Check the contents of the data frame ########################## summary(FloweringStems.df) # quick summaries of contents # note difference between # qualitative variables (= factors) # quantitative variables (= numerical vars.) head(FloweringStems.df) # list only top or bottom rows of data frame tail(FloweringStems.df)

########################## # Determine Stem Counts to each of the Plots ##########################

TotalF <- sum (FloweringStems.df[2:9409,5:14], na.rm=TRUE) TotalF #9284 total stems

#A1stems.df <-subset(FloweringStems.df, PLOT=="A1") #A1stems.df

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#A1total <- sum (A1stems.df[,5:14], na.rm=TRUE) #A1total #75

####################################################################### ###### Notched boxplot of Number of Stems against two crossed factors ###### (Treatment Time and Treatment Frequency) ###### boxes colored for ease of interpretation ######################################################################## boxplot(log10(Sum+1)~Treatment, data=FloweringStems.df, notch=TRUE, col=(c("gold","darkgreen")), xaxt="n", xlab="Treatment", ylab="Log of Number of Stems") #main="Number of Stems Against 2 Crossed Treatment Factors", legend("topleft", inset=.02, title="Treatment Frequency", c("Annual","Biennial"), fill=(c("gold","darkgreen")), horiz=TRUE, cex=0.7) axis(1, at=(c(1.5, 3.5)), labels=(c("Early", "Late"))) abline(v=c(2.5),lty=2, col="blue")

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############# VIOLIN PLOTS ################# library(vioplot) x1 <- log10(FloweringStems.df$Sum[FloweringStems.df$Treatment=="EA"]+1) x2 <- log10(FloweringStems.df$Sum[FloweringStems.df$Treatment=="EB"]+1) x3 <- log10(FloweringStems.df$Sum[FloweringStems.df$Treatment=="LA"]+1) x4 <- log10(FloweringStems.df$Sum[FloweringStems.df$Treatment=="LB"]+1) vioplot(x1, x2, x3,x4, names=c("EA", "EB", "LA", "LB"),col=(c("gold","darkgreen"))) title(ylab="log10 of Number of Stems", xlab="Treatments") # title("Violin Plots of Number of Stems per Treatment") #xlab="Treatment", ylab="Number of Stems") #how do i read this? legend("topleft", inset=.02, title="Treatments", c("EA = Early Annual ","EB = Early Biennial ", "LA = Late Annual ","LB = Late Biennial "), #horiz=TRUE, cex=0.7)

# In the violin plot of the log of the number of stems in each treatment, the empty dot depicts the median; the bolded black line delineates the first (lower) and third (higher) quartiles; the thin black line marks the upper and lower adjacent values; and the violin plot thins at the outside values (Figure 12).

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> TukeyHSD(StemsAnova) Tukey multiple comparisons of means 95% family-wise confidence level

Fit: aov(formula = Sum ~ Treatment, data = FloweringStems.df)

$Treatment diff lwr upr p adj EB-EA -0.01128472 -0.2556460 0.2330765 0.9994030 LA-EA 1.73914931 1.4995334 1.9787652 0.0000000 LB-EA 1.83593750 1.5915763 2.0802987 0.0000000 LA-EB 1.75043403 1.5108181 1.9900499 0.0000000 LB-EB 1.84722222 1.6028610 2.0915835 0.0000000 LB-LA 0.09678819 -0.1428277 0.3364041 0.7271479

# Number of flowering stems per quadrats par (mfrow=c(2, 2)) EA <- log10(FloweringStems.df$Sum[FloweringStems.df$Treatment=="EA"]+1) EB <- log10(FloweringStems.df$Sum[FloweringStems.df$Treatment=="EB"]+1) LA <- log10(FloweringStems.df$Sum[FloweringStems.df$Treatment=="LA"]+1) LB <- log10(FloweringStems.df$Sum[FloweringStems.df$Treatment=="LB"]+1) hist(EA, main="Early Annual", xlab="log of stems per quadrat", col="gold") hist(EB, main="Early Biennial", xlab="log of stems per quadrat", col="darkgreen") hist(LA, main="Late Annual", xlab="log of stems per quadrat", col="gold") hist(LB, main="Late Biennial", xlab="log of stems per quadrat", col="darkgreen") #label as counts of # flowering stems per 4msquared quadrats

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#distribution of flowering stems with >1 one stem par (mfrow=c(2, 2)) b1 <- (FloweringStems.df$Treatment=="EA" & FloweringStems.df$Sum >0) b2 <-(FloweringStems.df$Treatment=="EB" & FloweringStems.df$Sum >0) b3 <-(FloweringStems.df$Treatment=="LA" & FloweringStems.df$Sum >0) b4 <- (FloweringStems.df$Treatment=="LB" & FloweringStems.df$Sum >0) x1 <- log10(FloweringStems.df$Sum[b1]) x2 <- log10(FloweringStems.df$Sum[b2]) x3 <- log10(FloweringStems.df$Sum[b3]) x4 <- log10(FloweringStems.df$Sum[b4]) hist(x1, xlim=c(0.0,1.7), ylim=c(0,140), main="Early Annual", xlab="log of stems per quadrat", col="gold") hist(x2, xlim=c(0.0,1.7), ylim=c(0,140), main="Early Biennial", xlab="log of stems per quadrat", col="darkgreen") hist(x3, xlim=c(0.0,1.7), ylim=c(0,140), main="Late Annual", xlab="log of stems per quadrat", col="gold")

130 hist(x4, xlim=c(0.0,1.7), ylim=c(0,140), main="Late Biennial", xlab="log of stems per quadrat", col="darkgreen") par (mfrow=c(1, 1))

> ####################### ANOVA STATS ########################## > > > aggregate(FloweringStems.df$Sum, list(FloweringStems.df$PLOT), mean, na.rm=TRUE) Group.1 x 1 A1 0.130208333 2 A2 0.086805556 3 A3 5.560763889 4 A4 5.039930556 5 B1 0.128472222 6 B2 0.053819444 7 B3 1.951388889 8 B4 0.788194444 9 C1 0.435763889 10 C2 0.036458333

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11 C3 0.135416667 12 C4 0.126736111 13 D1 0.003472222 14 D2 1.209635417 15 D3 0.017361111 16 D4 0.010416667 > #put table into an object > StemCountbyPlot.df<-aggregate(FloweringStems.df$Sum, list(FloweringStems.df$PLOT), mean, na.rm=TRUE) > StemCountbyPlot.df Group.1 x 1 A1 0.130208333 2 A2 0.086805556 3 A3 5.560763889 4 A4 5.039930556 5 B1 0.128472222 6 B2 0.053819444 7 B3 1.951388889 8 B4 0.788194444 9 C1 0.435763889 10 C2 0.036458333 11 C3 0.135416667 12 C4 0.126736111 13 D1 0.003472222 14 D2 1.209635417 15 D3 0.017361111 16 D4 0.010416667 > > time<-c("E", "E", "L","L","E","E","L","L", "L","E","L","E","E","L","E","L") > freq<-c("A","B","B","A","B","A","B","A","A","B","B","A","A","A","B","B") > > newdf<-data.frame(cbind(StemCountbyPlot.df,time,freq)) > newdf Group.1 x time freq 1 A1 0.130208333 E A 2 A2 0.086805556 E B 3 A3 5.560763889 L B 4 A4 5.039930556 L A 5 B1 0.128472222 E B 6 B2 0.053819444 E A 7 B3 1.951388889 L B 8 B4 0.788194444 L A 9 C1 0.435763889 L A 10 C2 0.036458333 E B

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11 C3 0.135416667 L B 12 C4 0.126736111 E A 13 D1 0.003472222 E A 14 D2 1.209635417 L A 15 D3 0.017361111 E B 16 D4 0.010416667 L B > > m_1 <- aov(x ~ 1, data=newdf) > m_2 <-aov(x ~ time, data = newdf) > m_3 <- aov(x ~ freq, data = newdf) > m_4 <-aov(x ~ time + freq, data=newdf) > m_5 <-aov(x~ time*freq, data=newdf) > AIC(m_1) #66.66 [1] 66.66417 > AIC(m_2) #63.4 lowest AIC* [1] 63.38261 > AIC(m_3) #68.66 [1] 68.66376 > AIC(m_4) #65.4 [1] 65.38204 > AIC(m_5) #67.4 [1] 67.38048 > summary(m_1) #doesn't show pvalue Df Sum Sq Mean Sq F value Pr(>F) Residuals 15 47.05 3.137 > summary(m_2) #pval=0.0346 (lowest AIC value! yay!) Df Sum Sq Mean Sq F value Pr(>F) time 1 13.23 13.228 5.475 0.0346 * Residuals 14 33.82 2.416 --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 > summary(m_3) # freq not signif Df Sum Sq Mean Sq F value Pr(>F) freq 1 0.00 0.001 0 0.985 Residuals 14 47.05 3.361 > summary(m_4) # time is signif Df Sum Sq Mean Sq F value Pr(>F) time 1 13.23 13.228 5.085 0.042 * freq 1 0.00 0.001 0.000 0.983 Residuals 13 33.82 2.602 --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 > summary(m_5) # time is signif Df Sum Sq Mean Sq F value Pr(>F)

133 time 1 13.23 13.228 4.694 0.0511 . freq 1 0.00 0.001 0.000 0.9838 time:freq 1 0.00 0.003 0.001 0.9733 Residuals 12 33.82 2.818 --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 > > ################# > l_1 <- aov(log10(x) ~ 1, data=newdf) > l_2 <-aov(log10(x) ~ time, data = newdf) > l_3 <- aov(log10(x) ~ freq, data = newdf) > l_4 <-aov(log10(x) ~ time + freq, data=newdf) > l_5 <-aov(log10(x)~ time*freq, data=newdf) > AIC(l_1) [1] 46.16254 > AIC(l_2) #lowest AIC* [1] 39.93281 > AIC(l_3) [1] 47.91914 > AIC(l_4) [1] 41.5236 > AIC(l_5) [1] 42.68385 > summary(l_1) #doesn't show pvalue Df Sum Sq Mean Sq F value Pr(>F) Residuals 15 13.06 0.8709 > summary(l_2) #pval=0.0346 (lowest AIC value! yay!) Df Sum Sq Mean Sq F value Pr(>F) time 1 5.253 5.253 9.416 0.00834 ** Residuals 14 7.811 0.558 --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 > summary(l_3) # freq not signif Df Sum Sq Mean Sq F value Pr(>F) freq 1 0.197 0.1972 0.215 0.65 Residuals 14 12.867 0.9191 > summary(l_4) # time is signif Df Sum Sq Mean Sq F value Pr(>F) time 1 5.253 5.253 8.970 0.0103 * freq 1 0.197 0.197 0.337 0.5716 Residuals 13 7.614 0.586 --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 > summary(l_5) # time is signif

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Df Sum Sq Mean Sq F value Pr(>F) time 1 5.253 5.253 8.726 0.0121 * freq 1 0.197 0.197 0.328 0.5776 time:freq 1 0.389 0.389 0.647 0.4370 Residuals 12 7.224 0.602 --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 > anova(l_1, l_2) Analysis of Variance Table

Model 1: log10(x) ~ 1 Model 2: log10(x) ~ time Res.Df RSS Df Sum of Sq F Pr(>F) 1 15 13.0641 2 14 7.8108 1 5.2533 9.4159 0.008337 ** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 > #(F(1,14) = 5.375)

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Part B: SpatialDistribAllPlots.R

# Use this script to make the spatial distribution maps of all plots. # MUST CHANGE CODE FOR EACH INDIV PLOT

####### # CLEAN UP ####### ls() rm(list=ls()) setwd("/Users/Julie/Desktop/Thesis")#Set your own working directory here. getwd() ls() library(plyr) #################### # # FUNCTION SECTION # ####################

# Function to plot a square # replace with polygon # arg x, y - lower left coordinates # len - length of side # returns plots a square plot_sq<-function(x,y,len){ x.v<-c(x,x+len,x+len,x,x) y.v<-c(y,y,y+len,y+len,y) points(y.v~x.v,type="l") }

# Place plant location function - Data Frame as Argument plantsdf.fn<-function(plant.df){ xp<-runif(plant.df$np,plant.df$x,plant.df$x+plant.df$len) yp<-runif(plant.df$np,plant.df$y,plant.df$y+plant.df$len) return(plntxy.df=data.frame(plant.df$case.no,xp,yp)) }

#################### # # SET UP MAPPING DATAFRAMES # ####################

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# FIELD, PLOT AND GRID REFERENCE DATA

# field data NPlots<-16; Plot.side<-24; Grid.len<-12; Grid.offset<-6; Field.df<-data.frame(NPlots,Plot.side,Grid.len,Grid.offset)

# Plot Data Reference Data Frame - plot names, ttmts, lower left x, y row and columns Plot.nms<- c("A1","A2","A3","A4","B1","B2","B3","B4","C1","C2","C3","C4","D1","D2","D3","D 4") Plot.ttmt<- c("EA","EB","LB","LA","EB","EA","LB","LA","LA","EB","LB","EA","EA","LA","EB ","LB") Plot.block<-c(rep("A",4),rep("B",4),rep("C",4),rep("D",4)) Plot.X<-c(0,0,0,0,0,1,2,3,3,3,3,3,1,1,2,2) Plot.Y<-c(0,1,2,3,4,4,4,4,3,2,1,0,2,3,3,2) Plot.x<-Plot.X*26; Plot.y<-Plot.Y*26 Plot.df<-data.frame(Plot.nms,Plot.ttmt,Plot.block,Plot.X,Plot.Y,Plot.x,Plot.y)

# Grid Data Reference Data frame rows/columns and meter displacements grid.v<-seq(0,11) cdval.v<-2*grid.v Grid.df<-data.frame(grid.v,cdval.v) save(Field.df,Plot.df,Grid.df,file="07_Field Data")

#################### # # PROGRAM SECTION # ####################

############ # READ DATA ############ load("06_AllPlots_Quads") ls()

# Check Data # summary(PlotA3.df) summary(AllPlotsQuads.df) dim(AllPlotsQuads.df) summary(Plot.df) dim(Plot.df)

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################### # # PART 1 - MAKE A MAP OF THE WHOLE STUDY AREA

###################

################ # MAKE THE OVERALL MAP OF THE STUDY AREA ################ width<-4*26-2; height<- 5*26-2; # x and y field maxima in meters x<-c(0,width); y<-c(0,height); # lay out the map area plot(y~x,type="n",xlim=c(0,width),ylim=c(0,height),xlab="meters",ylab="meters",asp=1 ) #draw the plots and write the labels for (i in 1:NPlots){ plot_sq(Plot.df$Plot.x[i],Plot.df$Plot.y[i],Plot.side) plot_sq(Plot.df$Plot.x[i]+Grid.offset,Plot.df$Plot.y[i]+Grid.offset,Grid.len) text(Plot.df$Plot.x+6,Plot.df$Plot.y+2,Plot.nms) text(Plot.df$Plot.x+18,Plot.df$Plot.y+2,Plot.ttmt) } # add the scale bar legend(32,20,c("20 meters"),bty="n") x0<-c(50,50); y0<-c(20,20); x1<-c(40,60); y1<-c(20,20); arrows(x0,y0,x1,y1,length=0.1,angle=90)

################################################################## ################################################################## ################################################################## ################################################################## ################################################################## ################ CHANGE PLOT NUMBER HERE ################### ################################################################## ################################################################## ################################################################## ################################################################## ################################################################## ##################################################################

# CHOOSE AND ASSIGN A NEW QUADRAT TO MAP

# PICK THE PLOT HERE:

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FP<-"D4" # THIS PICKS PLOT D4 - SUBSTITUTE OTHER VALUES E.G. "A4", "B1" etc. to make maps of other plots

# EXTRACT THE FLOWER DATA FOR THE FOCAL PLOT FocalPlot.df<-subset(AllPlotsQuads.df,PLOT==FP) # SET THE COORDINATES FOR THE FOCAL PLOT xmin<-Plot.df$Plot.x[Plot.nms==FP] ymin<-Plot.df$Plot.y[Plot.nms==FP]

################ # Assign Locations to Plants on Focal Plot ################ head(FocalPlot.df); tail(FocalPlot.df); # Check the data

# Set up data frame to assign plant locations

# 1. add casenumber variable and quadrat length variable case.no<-seq(from=1,to=dim(FocalPlot.df)[1],by=1) len.5<-0.5 # quadrat side length qdlen<-rep(len.5,dim(FocalPlot.df)[1]) FocalPlot.df<-data.frame(case.no,FocalPlot.df,qdlen)

# 2. Extract counts and lower left quadrat coordinates data for focal species (here, S.giga) Sgiga.df<-subset(FocalPlot.df,Sgiga>0,select=c(case.no,Sgiga,glbl.x,glbl.y,qdlen))

# 3. Change data set names for input into ddply() function colnames(Sgiga.df)<-c("case.no","np","x","y","len")

# 4. Check the data frame head(Sgiga.df)

Sgiga.df

# Now position each individual plant randomly within its quadrat head(Sgiga.df) Sgigaxy.df<-ddply(Sgiga.df,.(case.no),.fun=plantsdf.fn) summary(Sgigaxy.df)

################ # Make the map of The Focal Plot ################ xleft<-6; xright<-18; ylow<-6; ytop<-18; x<-c(xmin+xleft,xmin+xright,xmin+xright,xmin+xleft) # bug here? last xmin

139 y<-c(ymin+ylow,ymin+ylow,ymin+ytop,ymin+ytop) plot(y~x,type="n",xlim=c(x[1],x[2]),ylim=c(y[1],y[3])) xq<-seq(from=xmin+xleft,to=xmin+xright,by=0.5) yq<-seq(from=ymin+ylow,to=ymin+ytop,by=0.5) grid.df<-expand.grid(xq,yq) colnames(grid.df)<-c("x1","y1") points(y1~x1,data=grid.df,pch=19,cex=0.2) abline(v=(seq(xmin+xleft,xmin+xright,0.5)), col="deepskyblue2", lty="dotted") abline(h=(seq(ymin+ylow,ymin+ytop,0.5)), col="deepskyblue2", lty="dotted") xbnd<-c(xmin+xleft,xmin+xright,xmin+xright,xmin+xleft); ybnd<- c(ymin+ylow,ymin+ylow,ymin+ytop,ymin+ytop); polygon(xbnd,ybnd)

# Plot the plant locations points(Sgigaxy.df$xp,Sgigaxy.df$yp,col="red",pch=19,cex=0.5)

# Plot a second species of plant Euthamia graminifolia Egram.df<-subset(FocalPlot.df,Egram>0,select=c(case.no,Egram,glbl.x,glbl.y,qdlen)) colnames(Egram.df)<-c("case.no","np","x","y","len") head(Egram.df) Egramxy.df<-ddply(Egram.df,.(case.no),.fun=plantsdf.fn) points(Egramxy.df$xp,Egramxy.df$yp,col="blue",pch=19,cex=0.5)

# Plot a third species of plant Soidago altissima Salt.df<-subset(FocalPlot.df,Salt>0,select=c(case.no,Salt,glbl.x,glbl.y,qdlen)) colnames(Salt.df)<-c("case.no","np","x","y","len") head(Salt.df) Saltxy.df<-ddply(Salt.df,.(case.no),.fun=plantsdf.fn) points(Saltxy.df$xp,Saltxy.df$yp,col="green",pch=19,cex=0.5)

# Plot a Fourth species of plant Soidago rugosa Srug.df<-subset(FocalPlot.df,Srug>0,select=c(case.no,Srug,glbl.x,glbl.y,qdlen)) colnames(Srug.df)<-c("case.no","np","x","y","len") head(Srug.df) Srugxy.df<-ddply(Srug.df,.(case.no),.fun=plantsdf.fn) points(Srugxy.df$xp,Srugxy.df$yp,col="purple",pch=19,cex=0.5)

# Can save the plant coordinates: save(Sgigaxy.df,Egramxy.df,Saltxy.df,Srugxy.df,file="08_Plant Locns")

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Part C: Clumping.R ls() rm(list=ls()) ls() setwd('/Users/juliejung/Desktop/SR YR/Thesis') getwd() ls() library(lsr) library(plotrix) library(plyr)

# read.csv(file="my.csv.filename") Clone.df<-read.csv(file="ClumpingData.csv") Clone.df

#Plot<-c(rep("A1",6),rep("A2",6),rep("A3",6),rep("A4",6)) #Ttmt<-c(rep("E",12),rep("C",12)) #clump<-c(rnorm(6,4,1),rnorm(6,10,1),rnorm(6,12,1),rnorm(6,14,1)) #test.df<-data.frame(Plot,Ttmt,clump)

#Make two Box Plots side by side #par(mfrow=c(1,2)) #par(mfrow=c(1,1))

#individually

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#area by treatment boxplot(log10(Clone.df$Area)~Treatment,data=Clone.df,notch=TRUE,col=(c("gold","da rkgreen")), xaxt="n", ylab="Log of Clump Size",xlab="Treatment", main="Clump Sizes by Treatment") legend("topleft", inset=.02, title="Treatment Frequency", c("Annual","Biennial"), fill=(c("gold","darkgreen")), horiz=TRUE, cex=0.7) axis(1, at=(c(1.5, 3.5)), labels=(c("Early", "Late"))) abline(v=c(2.5),lty=2, col="blue")

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#patch size by plot boxplot(log10(Clone.df$Area)~Block,data=Clone.df,notch=TRUE,ylab="Log of Patch Size",xlab="Plot", col=c("gold","gold","darkgreen", "darkgreen", "gold","gold","darkgreen", "darkgreen", "darkgreen", "gold", "darkgreen", "gold", "gold", "darkgreen", "gold", "darkgreen")) legend("bottomright", inset=.02, title="Treatment Time", c("Early","Late"), fill=(c("gold","darkgreen")), horiz=TRUE, cex=0.7) abline(v=c(4.5, 8.5, 12.5),lty=2, col="blue")

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#look at clumping according to species

#by plot boxplot(log10(Clone.df$Area)~Treatment*Species,data=Clone.df,notch=TRUE,xaxt="n" , col=(c("gold","darkgreen", "blue", "red")), ylab="Log of Clump Size", xlab="Species", main="Clump Sizes by Species", las=2) axis(1, at=(c(2.5, 6.5, 10.5, 15)), labels=(c("Egram", "Salt", "Sgiga", "Srug"))) abline(v=c(4.5,8.5, 12.5),lty=2, col="blue") legend("bottomleft", inset=.02, title="Mowing Treatment", c("EA","EB", "LA", "LB"), fill=(c("gold","darkgreen", "blue", "red")), horiz=TRUE, cex=0.7)

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#by treatment boxplot(log10(Clone.df$Area)~Species,data=Clone.df,notch=TRUE,ylab="Log of Clump Size", xlab="Species", main="Clump Sizes by Species")

> summary(Clone.df) Block Treatment Time Freq Area Species A3 :58 EA: 36 E: 73 A:143 Min. : 0.000 Egram:53 A4 :54 EB: 37 L:199 B:129 1st Qu.: 0.010 Salt :39 D2 :37 LA:107 Median : 1.000 Sgiga:86 B3 :32 LB: 92 Mean : 2.051 Srug :94 C4 :18 3rd Qu.: 2.125 A2 :12 Max. :27.000 (Other):61 > > #calculate mean clumping area per treatment > aggregate(Area~Treatment,Clone.df,mean) Treatment Area

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1 EA 0.6155556 2 EB 0.3972973 3 LA 2.4321495 4 LB 2.8332609 > #by plot > aggregate(Area~Treatment*Block,Clone.df,mean) Treatment Block Area 1 EA A1 0.4050000 2 EB A2 0.7525000 3 LB A3 3.0789655 4 LA A4 2.6781481 5 EB B1 0.2150000 6 EA B2 0.5757143 7 LB B3 2.4400000 8 LA B4 3.5520000 9 LA C1 2.8333333 10 EB C2 0.1514286 11 LB C3 3.0000000 12 EA C4 0.7822222 13 EA D1 0.0000000 14 LA D2 1.7054054 15 EB D3 0.3383333 16 LB D4 1.0000000 > #species by treatment > aggregate(Area~Species,Clone.df,mean) Species Area 1 Egram 2.522453 2 Salt 1.528974 3 Sgiga 2.978372 4 Srug 1.152128 > #Species by plot > aggregate(Area~Treatment*Species,Clone.df,mean) Treatment Species Area 1 EB Egram 0.09166667 2 LA Egram 2.79151515 3 LB Egram 2.93000000 4 EA Salt 0.50000000 5 EB Salt 1.33666667 6 LA Salt 0.67166667 7 LB Salt 2.04608696 8 EA Sgiga 0.01000000 9 EB Sgiga 0.60300000 10 LA Sgiga 3.11641026 11 LB Sgiga 3.67285714

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12 EA Srug 0.65575758 13 EB Srug 0.22833333 14 LA Srug 1.67478261 15 LB Srug 2.20150000 > > #calculate confidence intervals > #treatment > aggregate(Area~Treatment,Clone.df,ciMean) Treatment Area.1 Area.2 1 EA 0.2683259 0.9627852 2 EB 0.1976202 0.5969744 3 LA 1.7153532 3.1489459 4 LB 1.9158678 3.7506540 > #plot > aggregate(Area~Treatment*Block,Clone.df,ciMean) Treatment Block Area.1 Area.2 1 EA A1 -0.032106550 0.842106550 2 EB A2 0.220731251 1.284268749 3 LB A3 1.767557574 4.390373460 4 LA A4 1.537289246 3.819007051 5 EB B1 0.005485592 0.424514408 6 EA B2 0.086306948 1.065121624 7 LB B3 1.218237047 3.661762953 8 LA B4 -0.111819116 7.215819116 9 LA C1 -0.273894197 5.940560863 10 EB C2 -0.194634676 0.497491819 11 LB C3 NA NA 12 EA C4 0.116271770 1.448172674 13 EA D1 NA NA 14 LA D2 0.843828412 2.566982399 15 EB D3 -0.084963614 0.761630281 16 LB D4 NA NA > > #species by treatment > aggregate(Area~Species,Clone.df,ciMean) Species Area.1 Area.2 1 Egram 1.2083036 3.8366021 2 Salt 0.8361566 2.2217921 3 Sgiga 2.0030140 3.9537302 4 Srug 0.7978608 1.5063946 > #Species by plot > aggregate(Area~Treatment*Species,Clone.df,ciMean) Treatment Species Area.1 Area.2 1 EB Egram -0.11826418 0.30159752

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2 LA Egram 0.82240626 4.76062404 3 LB Egram 0.93752022 4.92247978 4 EA Salt NA NA 5 EB Salt -2.44708635 5.12041968 6 LA Salt 0.11029558 1.23303775 7 LB Salt 0.93893500 3.15323892 8 EA Sgiga 0.01000000 0.01000000 9 EB Sgiga 0.23635603 0.96964397 10 LA Sgiga 2.11729938 4.11552113 11 LB Sgiga 1.54541033 5.80030395 12 EA Srug 0.27934634 1.03216881 13 EB Srug 0.07756344 0.37910323 14 LA Srug 1.00209095 2.34747427 15 LB Srug 0.95135202 3.45164798 Warning message: In computeCI(x, conf, na.rm) : data have zero variance > > > #calculate standard deviation > aggregate(Area~Treatment,Clone.df,sd) Treatment Area 1 EA 1.0262399 2 EB 0.5988816 3 LA 3.7398413 4 LB 4.4298362 > #plot > aggregate(Area~Treatment*Block,Clone.df,sd) Treatment Block Area 1 EA A1 0.6110328 2 EB A2 0.8369438 3 LB A3 4.9875444 4 LA A4 4.1797748 5 EB B1 0.3297520 6 EA B2 0.5291773 7 LB B3 3.3887175 8 LA B4 5.1216660 9 LA C1 2.9608557 10 EB C2 0.3741848 11 LB C3 NA 12 EA C4 1.3391637 13 EA D1 NA 14 LA D2 2.5840855 15 EB D3 0.4033567 16 LB D4 NA

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> > #species by treatment > aggregate(Area~Species,Clone.df,sd) Species Area 1 Egram 4.767730 2 Salt 2.137255 3 Sgiga 4.549235 4 Srug 1.729652 > #Species by plot > aggregate(Area~Treatment*Species,Clone.df,sd) Treatment Species Area 1 EB Egram 0.2000417 2 LA Egram 5.5532840 3 LB Egram 3.4508817 4 EA Salt NA 5 EB Salt 1.5231656 6 LA Salt 0.8835346 7 LB Salt 2.5602889 8 EA Sgiga 0.0000000 9 EB Sgiga 0.5125329 10 LA Sgiga 3.0821297 11 LB Sgiga 6.1932238 12 EA Srug 1.0615556 13 EB Srug 0.3031841 14 LA Srug 1.5555995 15 LB Srug 2.6711741

> # ANOVA > m_1 <- aov(Area ~ 1, data=Clone.df) > m_2 <-aov(Area ~ Time, data = Clone.df) > m_3 <- aov(Area ~ Freq, data = Clone.df) > m_4 <-aov(Area ~ Time + Freq, data=Clone.df) > m_5 <-aov(Area~ Time*Freq, data=Clone.df) > AIC(m_1) #1475.8 [1] 1475.811 > AIC(m_2) #1459.0 lowest AIC* [1] 1458.989 > AIC(m_3) #1477.7 [1] 1477.679 > AIC(m_4) #1460.7 [1] 1460.685 > AIC(m_5) #1462.3 [1] 1462.266 > summary(m_1) #doesn't show pvalue

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Df Sum Sq Mean Sq F value Pr(>F) Residuals 271 3565 13.16 > summary(m_2) #pval= 1.57e-5 (lowest AIC value! yay!) Df Sum Sq Mean Sq F value Pr(>F) Time 1 238 238.38 19.35 1.57e-05 *** Residuals 270 3327 12.32 --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 > summary(m_3) # freq not signif Df Sum Sq Mean Sq F value Pr(>F) Freq 1 2 1.731 0.131 0.718 Residuals 270 3564 13.198 > summary(m_4) # time is signif Df Sum Sq Mean Sq F value Pr(>F) Time 1 238 238.38 19.296 1.61e-05 *** Freq 1 4 3.71 0.301 0.584 Residuals 269 3323 12.35 --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 > summary(m_5) # time is signif Df Sum Sq Mean Sq F value Pr(>F) Time 1 238 238.38 19.254 1.65e-05 *** Freq 1 4 3.71 0.300 0.584 Time:Freq 1 5 5.11 0.413 0.521 Residuals 268 3318 12.38 --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 > #LOG > l_1 <- aov(log10(Area+1) ~ 1, data=Clone.df) > l_2 <-aov(log10(Area+1) ~ Time, data = Clone.df) > l_3 <- aov(log10(Area+1) ~ Freq, data = Clone.df) > l_4 <-aov(log10(Area+1) ~ Time + Freq, data=Clone.df) > l_5 <-aov(log10(Area+1)~ Time*Freq, data=Clone.df) > AIC(l_1) [1] 157.543 > AIC(l_2) [1] 119.4793 > AIC(l_3) [1] 159.5396 > AIC(l_4) [1] 121.3748 > AIC(l_5) [1] 122.5848

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> summary(l_1) Df Sum Sq Mean Sq F value Pr(>F) Residuals 271 28.01 0.1033 > summary(l_2) Df Sum Sq Mean Sq F value Pr(>F) Time 1 3.836 3.836 42.85 2.97e-10 *** Residuals 270 24.170 0.090 --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 > summary(l_3) Df Sum Sq Mean Sq F value Pr(>F) Freq 1 0.00 0.00036 0.003 0.953 Residuals 270 28.01 0.10372 > summary(l_4) Df Sum Sq Mean Sq F value Pr(>F) Time 1 3.836 3.836 42.705 3.18e-10 *** Freq 1 0.009 0.009 0.103 0.748 Residuals 269 24.161 0.090 --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 > summary(l_5) Df Sum Sq Mean Sq F value Pr(>F) Time 1 3.836 3.836 42.670 3.24e-10 *** Freq 1 0.009 0.009 0.103 0.748 Time:Freq 1 0.070 0.070 0.780 0.378 Residuals 268 24.091 0.090 --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

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Part D: Height.R # # Height of Flowering Stems R analysis - Undergraduate Thesis, 2015 # 22 April 2015 # # INITIALIZE WORKING DIRECTORY AND CLEAR OUT OLD DATA # set the working directory, and check that working directory is correct rm(list=ls()) ls() setwd('/Users/juliejung/Desktop/SR YR/Thesis') getwd() # check this to see that you have the correct directory!

# READ THE DATA INTO A DATA FRAME AND CHECK THE DATA # First Read in and extract the desired data ########################## Height.df<-read.csv("Height.csv") # read the data into a data frame Height.df # type out the data frame contents class(Height.df$Treatment) class(Height.df$Height) NumHeight<-as.numeric(Height.df$Height)

152 boxplot(NumHeight~Treatment, data=Height.df, notch=TRUE, col=(c("gold","darkgreen")), xaxt="n", xlab="Treatment", ylab="Height of Tallest Plant in Quadrat (cm)") legend("topleft", inset=.02, title="Treatment Frequency", c("Annual","Biennial"), fill=(c("gold","darkgreen")), horiz=TRUE, cex=0.7) axis(1, at=(c(1.5, 3.5)), labels=(c("Early", "Late"))) abline(v=c(2.5),lty=2, col="blue")

########### ANOVA #####################

HeightbyPlot.df<-aggregate(NumHeight, list(Height.df$PLOT), mean, na.rm=TRUE) HeightbyPlot.df time<-c("E", "E", "L","L","E","E","L","L", "L","E","L","E","E","L","E","L") freq<-c("A","B","B","A","B","A","B","A","A","B","B","A","A","A","B","B") newdf<-data.frame(cbind(HeightbyPlot.df,time,freq)) newdf

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Group.1 x time freq 1 A1 19.83333 E A 2 A2 13.69444 E B 3 A3 103.52778 L B 4 A4 91.27778 L A 5 B1 19.05556 E B 6 B2 17.41667 E A 7 B3 94.16667 L B 8 B4 89.22222 L A 9 C1 80.00000 L A 10 C2 17.05556 E B 11 C3 42.77778 L B 12 C4 17.72222 E A 13 D1 25.00000 E A 14 D2 74.30000 L A 15 D3 26.12500 E B 16 D4 57.10000 L B m_1 <- aov(x ~ 1, data=newdf) m_2 <-aov(x ~ time, data = newdf) m_3 <- aov(x ~ freq, data = newdf) m_4 <-aov(x ~ time + freq, data=newdf) m_5 <-aov(x~ time*freq, data=newdf) > AIC(m_1) #161.1 [1] 161.0986 > AIC(m_2) #135.27 lowest AIC* [1] 135.27 > AIC(m_3) #162.999 [1] 162.9993 > AIC(m_4) #136.7 [1] 136.6964 > AIC(m_5) #138.3 [1] 138.3143 > summary(m_1) #doesn't show pvalue Df Sum Sq Mean Sq F value Pr(>F) Residuals 15 17212 1148 > summary(m_2) #pval=0.00000117(lowest AIC value! yay!) Df Sum Sq Mean Sq F value Pr(>F) time 1 14189 14189 65.71 1.17e-06 *** Residuals 14 3023 216 --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 > summary(m_3) # freq not signif

154

Df Sum Sq Mean Sq F value Pr(>F) freq 1 106 106.4 0.087 0.772 Residuals 14 17106 1221.8 > summary(m_4) # time is signif Df Sum Sq Mean Sq F value Pr(>F) time 1 14189 14189 63.239 2.39e-06 *** freq 1 106 106 0.474 0.503 Residuals 13 2917 224 --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 > summary(m_5) # time is signif Df Sum Sq Mean Sq F value Pr(>F) time 1 14189 14189 59.786 5.32e-06 *** freq 1 106 106 0.449 0.516 time:freq 1 69 69 0.290 0.600 Residuals 12 2848 237 --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 >

155

Part E: floweringstemdiversity.R

> # Calculate DIVERSITY INDICES for Flowering Stem Data > # and RAREFACTION analysis > # APRIL 2015 > > ################################################################# > # > # SECTION 1: DOWNLOAD AND LOAD PACKAGES > # lattice is on disc; get vegan from the Web (see Appendix) DELETE > # > #################################################################

> # INITIALIZE WORKING DIRECTORY AND CLEAR OUT OLD DATA > # set the working directory, and check that working directory is correct > setwd('/Users/juliejung/Desktop/SR YR/Thesis') > getwd() # check this to see that you have the correct directory! [1] "/Users/juliejung/Desktop/SR YR/Thesis" > > rm(list=ls()) # Clear the workspace > library(vegan) # Install two packages, vegan (which does standard diversity indices, H', J and rarefaction curve) and lattice (program for rapidly visualizing data). # Then load package vegan > library(lattice) # Load package lattice > library(nlme)

> ################################################################# > # > # SECTION 2: READ IN DATA > # These are in R data files and can only be read using load() command in R. > # Use summary() or head() to view contents of data frames that are in these files. > # > #################################################################

FloweringStems.df<-read.csv("FloweringStems.csv") # read the data into a data frame FloweringStems.df

> ################################################################# > # > # SECTION 3: PREPARE DATA > # > #################################################################

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> > # we've already got a dataframe of # of visitors of each taxa to each PlotPoint! > class(FloweringStems.df) [1] "data.frame"

> # Reformat the counts for entry into Vegan package > # as a matrix (not a data frame) > head(FloweringStems.df, n = 1) PLOT QUADX QUADY quad Treatment Sgiga Egram Srug Salt Scan 1 A1 3 3 1 EA 0 0 0 0 0 Slater Scord Aaltis Asyr Number Sum Other 1 0 0 0 0 0 0 > dim(FloweringStems.df) [1] 9408 17 > > # Condense Flowering Stem Dataframe > # remove the Other column - better would be to replace this > # with a separate column for each species in the column > # use aggregate to get a df with a sum for each plot > # save the plot and treatment value as vectors > # remove all but the numeric values from the df > # apply aggregate() by plot > > # convert FloweringStems.df to a matrix with: > # all numeric (remove the non-numeric columns) > # last column is the sum of each row > # rownames correspond to plot names > > > FloweringStems.df <- FloweringStems.df[, -c(2:5, 15:17)] > Flower_Ag_df <- aggregate(FloweringStems.df[ ,-1], + by = list(FloweringStems.df[ ,1]), FUN = sum, + na.rm = TRUE) > rownames(Flower_Ag_df) <- Flower_Ag_df[, 1] > Flower_Ag_df <- Flower_Ag_df[, -1] > Flower_Ag_M<-addmargins(as.matrix(Flower_Ag_df), 2) > > > ################################################################# > # > # SECTION 4: Calculate Diversity Stats (S, H, & J) > # using Vegan package functions > # > #################################################################

157

> > #df2Sum = Flower_Ag_M > #df2.Sum.df = Flower_Ag_df > > N<-Flower_Ag_M[,dim(Flower_Ag_M)[2]] > H<-diversity(Flower_Ag_df, index = "shannon", MARGIN = 1, base = exp(1)) > S<-specnumber(Flower_Ag_df) > J<-H/log(S) > # Hab<-levels(df2$row.names) > > # Gather estimates into a data frame (Div_Vals.df) > Div_Vals.df<-data.frame(N,S,H,J) > > # View diversity values in the data frame. This shows the habitat (Hab), # of individuals (N), family richness (S), Shannon-Weiner Diversity Index (H) and Evenness (J). > > treatment<-c("EA", "EB", "LB","LA","EB","EA","LB","LA", "LA","EB","LB","EA","EA","LA","EB","LB") > newdf<-data.frame(cbind(Div_Vals.df,treatment)) > newdf N S H J treatment A1 73 4 1.1114109 0.8017135 EA A2 49 6 1.4477193 0.8079875 EB A3 3201 7 1.2471192 0.6408925 LB A4 2901 5 1.1065267 0.6875237 LA B1 69 7 1.1830056 0.6079446 EB B2 30 4 0.9819716 0.7083428 EA B3 1124 5 0.9044870 0.5619894 LB B4 454 6 1.3137404 0.7332125 LA C1 251 5 0.9443440 0.5867539 LA C2 20 4 1.2206073 0.8804820 EB C3 78 2 0.6812657 0.9828587 LB C4 73 2 0.3734247 0.5387379 EA D1 2 1 0.0000000 NaN EA D2 929 7 1.0239971 0.5262304 LA D3 10 2 0.6108643 0.8812909 EB D4 6 1 0.0000000 NaN LB > > #New_Div_Vals.df

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> ################################################################# > # > # SECTION 5: Plot Diversity Stats (S, H, & J) & Run ANOVA > # using Vegan package functions > # > #################################################################

> boxplot(H~treatment, data=newdf, notch=TRUE, col=(c("gold", "darkgreen")), xaxt="n", ylim=c(0, 1.6), xlab="Treatment", ylab="Shannon-Weiner Diversity Index (H)") Warning message: In bxp(list(stats = c(0, 0.186712333375257, 0.677698155206068, 1.04669124690405, : some notches went outside hinges ('box'): maybe set notch=FALSE > legend("topleft", inset=.02, title="Treatment Frequency", c("Annual","Biennial"), fill=(c("gold","darkgreen")), horiz=TRUE, cex=0.65) > axis(1, at=(c(1.5, 3.5)), labels=(c("Early", "Late"))) > abline(v=c(2.5),lty=2, col="blue") >

159

> ########### ANOVA (H~Treatment) ##################### > > time<-c("E", "E", "L","L","E","E","L","L", "L","E","L","E","E","L","E","L") > freq<-c("A","B","B","A","B","A","B","A","A","B","B","A","A","A","B","B") > > divdf<-data.frame(cbind(newdf,time,freq)) > divdf N S H J treatment time freq A1 73 4 1.1114109 0.8017135 EA E A A2 49 6 1.4477193 0.8079875 EB E B A3 3201 7 1.2471192 0.6408925 LB L B A4 2901 5 1.1065267 0.6875237 LA L A B1 69 7 1.1830056 0.6079446 EB E B B2 30 4 0.9819716 0.7083428 EA E A B3 1124 5 0.9044870 0.5619894 LB L B B4 454 6 1.3137404 0.7332125 LA L A C1 251 5 0.9443440 0.5867539 LA L A C2 20 4 1.2206073 0.8804820 EB E B C3 78 2 0.6812657 0.9828587 LB L B C4 73 2 0.3734247 0.5387379 EA E A D1 2 1 0.0000000 NaN EA E A D2 929 7 1.0239971 0.5262304 LA L A D3 10 2 0.6108643 0.8812909 EB E B D4 6 1 0.0000000 NaN LB L B > > m_1 <- aov(H ~ 1, data=divdf) > m_2 <-aov(H ~ time, data = divdf) > m_3 <- aov(H ~ freq, data = divdf) > m_4 <-aov(H ~ time + freq, data=divdf) > m_5 <-aov(H~ time*freq, data=divdf) > AIC(m_1) #22.14 lowest AIC* [1] 22.13954 > AIC(m_2) #24.11 [1] 24.11012 > AIC(m_3) #24.07 [1] 24.07299 > AIC(m_4) #26.04 [1] 26.04346 > AIC(m_5) #22.96 * second lowest AIC [1] 22.95542 > summary(m_1) #doesn't show pvalue Df Sum Sq Mean Sq F value Pr(>F) Residuals 15 2.911 0.1941 > summary(m_2) #

160

Df Sum Sq Mean Sq F value Pr(>F) time 1 0.0053 0.00535 0.026 0.875 Residuals 14 2.9055 0.20753 > summary(m_3) # Df Sum Sq Mean Sq F value Pr(>F) freq 1 0.0121 0.01208 0.058 0.813 Residuals 14 2.8987 0.20705 > summary(m_4) # Df Sum Sq Mean Sq F value Pr(>F) time 1 0.0053 0.00535 0.024 0.879 freq 1 0.0121 0.01208 0.054 0.819 Residuals 13 2.8934 0.22257 > summary(m_5) # time: freq interaction. pval= 0.0556 (almost significant) Df Sum Sq Mean Sq F value Pr(>F) time 1 0.0053 0.0053 0.030 0.8643 freq 1 0.0121 0.0121 0.069 0.7975 time:freq 1 0.7882 0.7882 4.493 0.0556 . Residuals 12 2.1052 0.1754 --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 > > ###################################################

161

> ################################################### > > #can use either newdf or divdf now > > boxplot(J~treatment, data=divdf, notch=TRUE, col=(c("gold", "darkgreen")), xaxt="n", ylim=c(0.45, 1.0), xlab="Treatment", ylab="Evenness (J)") Warning message: In bxp(list(stats = c(0.538737914866582, 0.623540362584342, 0.708342810302103, : some notches went outside hinges ('box'): maybe set notch=FALSE > legend("bottomleft", inset=.02, title="Treatment Frequency", c("Annual","Biennial"), fill=(c("gold","darkgreen")), horiz=TRUE, cex=0.65) > axis(1, at=(c(1.5, 3.5)), labels=(c("Early", "Late"))) > abline(v=c(2.5),lty=2, col="blue")

> > ########### ANOVA (J~Treatment) ##################### > > m_1 <- aov(J ~ 1, data=divdf) > m_2 <-aov(J ~ time, data = divdf) > m_3 <- aov(J ~ freq, data = divdf)

162

> m_4 <-aov(J ~ time + freq, data=divdf) > m_5 <-aov(J~ time*freq, data=divdf) > AIC(m_1) [1] -11.68771 > AIC(m_2) [1] -10.68412 > AIC(m_3) #lowest AIC* [1] -12.17815 > AIC(m_4) [1] -10.91057 > AIC(m_5) [1] -8.925673 > summary(m_1) #doesn't show pvalue Df Sum Sq Mean Sq F value Pr(>F) Residuals 13 0.2673 0.02056 2 observations deleted due to missingness > summary(m_2) # Df Sum Sq Mean Sq F value Pr(>F) time 1 0.01836 0.01836 0.885 0.365 Residuals 12 0.24894 0.02074 2 observations deleted due to missingness > summary(m_3) # Df Sum Sq Mean Sq F value Pr(>F) freq 1 0.04356 0.04356 2.336 0.152 Residuals 12 0.22374 0.01865 2 observations deleted due to missingness > summary(m_4) # Df Sum Sq Mean Sq F value Pr(>F) time 1 0.01836 0.01836 0.951 0.350 freq 1 0.03660 0.03660 1.896 0.196 Residuals 11 0.21234 0.01930 2 observations deleted due to missingness > summary(m_5) #nothing sighif Df Sum Sq Mean Sq F value Pr(>F) time 1 0.01836 0.01836 0.866 0.374 freq 1 0.03660 0.03660 1.726 0.218 time:freq 1 0.00023 0.00023 0.011 0.919 Residuals 10 0.21211 0.02121 2 observations deleted due to missingness > ################################################### > ################################################### > ################################################### >

163

> boxplot(S~treatment, data=divdf, notch=TRUE, col=(c("gold", "darkgreen")), xaxt="n", ylim=c(0, 8), xlab="Treatment", ylab="Richness (S)") Warning message: In bxp(list(stats = c(1, 1.5, 3, 4, 4, 2, 3, 5, 6.5, 7, 5, 5, 5.5, : some notches went outside hinges ('box'): maybe set notch=FALSE > legend("topleft", inset=.02, title="Treatment Frequency", c("Annual","Biennial"), fill=(c("gold","darkgreen")), horiz=TRUE, cex=0.65) > axis(1, at=(c(1.5, 3.5)), labels=(c("Early", "Late"))) > abline(v=c(2.5),lty=2, col="blue") >

> ########### ANOVA (S~Treatment) ##################### > > m_1 <- aov(S ~ 1, data=divdf) > m_2 <-aov(S ~ time, data = divdf) > m_3 <- aov(S ~ freq, data = divdf) > m_4 <-aov(S ~ time + freq, data=divdf) > m_5 <-aov(S~ time*freq, data=divdf) > AIC(m_1) #lowest AIC* [1] 72.3197 > AIC(m_2)

164

[1] 73.33477 > AIC(m_3) [1] 74.3197 > AIC(m_4) [1] 75.33477 > AIC(m_5) [1] 72.64698 > summary(m_1) #doesn't show pvalue Df Sum Sq Mean Sq F value Pr(>F) Residuals 15 67 4.467 > summary(m_2) # Df Sum Sq Mean Sq F value Pr(>F) time 1 4 4.0 0.889 0.362 Residuals 14 63 4.5 > summary(m_3) # Df Sum Sq Mean Sq F value Pr(>F) freq 1 0 0.000 0 1 Residuals 14 67 4.786 > summary(m_4) # Df Sum Sq Mean Sq F value Pr(>F) time 1 4 4.000 0.825 0.38 freq 1 0 0.000 0.000 1.00 Residuals 13 63 4.846 > summary(m_5) #nothing sighif, time:freq is almost Df Sum Sq Mean Sq F value Pr(>F) time 1 4 4.000 1.021 0.3322 freq 1 0 0.000 0.000 1.0000 time:freq 1 16 16.000 4.085 0.0661 . Residuals 12 47 3.917 --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 > > ################################################### > # > # SECTION 6 : MAKE RARIFICATION CURVES FOR THE SAMPLES > # THESE CALCULATIONS MADE USING THE Vegan PACKAGE > # > ################################# > > # Function that sets to NA the ranks larger than the number in the sample > set.NA <- function(vct){ + max <- which.max(vct) + if(max == length(vct)) return(vct) + vct[(max+1):length(vct)] <- NA

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+ vct + } > > #df2Sum = Flower_Ag_M > #df2.Sum.df = Flower_Ag_df > # Calculate the RAREFACTION CURVES and prepare them for plotting > rarefy.v<-seq(1:max(divdf$N)) > rarefy.Samples<-rarefy(Flower_Ag_df[,],rarefy.v) > t.rarefy<-t(rarefy.Samples) > t.rarefy2<-apply(t.rarefy,2,set.NA) # set species counts beyond > ## sample size to NA t.rarefy2 > > # set up some plot parameters > xmax<-max(N); ymax<-max(S); > col.v<-c("blue","red","purple","green") > hab.v<-c("EA","EB","LA","LB") > N_S.df<-divdf[,c("N","S")] > > # Now draw the rarefaction curves (To be reported in your worksheet). > plot(rarefy.v,t.rarefy2[,9],xlab="Individuals Sampled", + ylab="Predicted Rarified Species",type="l",col=col.v[1],xlim=c(0,xmax), + ylim=c(0,ymax),lwd=3 > #rarefaction curve for stems > points(rarefy.v,t.rarefy2[,10],type="l",col=col.v[2],lwd=3) > points(rarefy.v,t.rarefy2[,11],type="l",col=col.v[3],lwd=3) > points(rarefy.v,t.rarefy2[,12],type="l",col=col.v[4],lwd=3) > points(N_S.df[9:12,"S"]~N_S.df[9:12,"N"],pch=3,col="black",cex=2,lwd=2) > legend(2500,2.3,legend=hab.v,lwd=3,col=col.v)

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#Rarefaction curve for Flowering Stems

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Part F: RankAbundanceStems.R

# Rank Abundance of Flowering Stems by Treatment R analysis - Undergraduate Thesis, 2015 # # 24 April 2015 # # INITIALIZE WORKING DIRECTORY AND CLEAR OUT OLD DATA # set the working directory, and check that working directory is correct rm(list=ls()) ls() setwd('/Users/juliejung/Desktop/SR YR/Thesis') getwd() # check this to see that you have the correct directory!

# READ THE DATA INTO A DATA FRAME AND CHECK THE DATA # First Read in and extract the desired data ########################## ########################## FloweringStems.df<-read.csv("FloweringStems.csv") # read the data into a data frame FloweringStems.df # type out the data frame contents ########################## ##########################

#par (mfrow=c(4,1)) #subset by treatment b1 <- subset(FloweringStems.df, Treatment=="EA", na.rm=TRUE, select=c(PLOT, Treatment, Sgiga, Egram, Srug, Salt, Scan, Slater, Scord, Aaltis, Asyr, Number)) b2 <- subset(FloweringStems.df, Treatment=="EB", na.rm=TRUE, select=c(PLOT, Treatment, Sgiga, Egram, Srug, Salt, Scan, Slater, Scord, Aaltis, Asyr, Number)) b3 <- subset(FloweringStems.df, Treatment=="LA", na.rm=TRUE, select=c(PLOT, Treatment, Sgiga, Egram, Srug, Salt, Scan, Slater, Scord, Aaltis, Asyr, Number)) b4 <- subset(FloweringStems.df, Treatment=="LB", na.rm=TRUE, select=c(PLOT, Treatment, Sgiga, Egram, Srug, Salt, Scan, Slater, Scord, Aaltis, Asyr, Number))

EA<- colSums(b1[3:12]) names(EA) <- c("Sgiga", "Egram", "Srug", "Salt", "Scan", "Slater", "Scord", "Aaltis", "Asyr", "Other") EB<- colSums(b2[3:12]) names(EB) <- c("Sgiga", "Egram", "Srug", "Salt", "Scan", "Slater", "Scord", "Aaltis", "Asyr", "Other") LA<- colSums(b3[3:12], na.rm=TRUE) names(LA) <- c("Sgiga", "Egram", "Srug", "Salt", "Scan", "Slater", "Scord", "Aaltis", "Asyr", "Other")

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LB<- colSums(b4[3:12]) names(LB) <- c("Sgiga", "Egram", "Srug", "Salt", "Scan", "Slater", "Scord", "Aaltis", "Asyr", "Other") par (mfrow=c(2,1)) barplot(EA, ylim=c(0,100), ylab="#Stems", xlab="Asteraceae Species", las=2, main="Early Annual") barplot(EB, ylim=c(0,100), ylab="#Stems", xlab="Asteraceae Species", las=2, main="Early Biennial")

par (mfrow=c(2,1)) barplot(LA, ylim=c(0,2500), ylab="#Stems", xlab="Asteraceae Species", las=2, main="Late Annual")

169 barplot(LB, ylim=c(0,2500), ylab="#Stems", xlab="Asteraceae Species", las=2, main="Late Biennial") par (mfrow=c(1, 1))

################################################## ## Sort from Most common to least common? ##################################################

#subset by treatment b1 <- subset(FloweringStems.df, Treatment=="EA", na.rm=TRUE, select=c(PLOT, Treatment, Sgiga, Egram, Srug, Salt, Scan, Slater, Scord, Aaltis, Asyr, Number)) b2 <- subset(FloweringStems.df, Treatment=="EB", na.rm=TRUE, select=c(PLOT, Treatment, Sgiga, Egram, Srug, Salt, Scan, Slater, Scord, Aaltis, Asyr, Number)) b3 <- subset(FloweringStems.df, Treatment=="LA", na.rm=TRUE, select=c(PLOT, Treatment, Sgiga, Egram, Srug, Salt, Scan, Slater, Scord, Aaltis, Asyr, Number))

170 b4 <- subset(FloweringStems.df, Treatment=="LB", na.rm=TRUE, select=c(PLOT, Treatment, Sgiga, Egram, Srug, Salt, Scan, Slater, Scord, Aaltis, Asyr, Number))

# find the sum of #stems in each taxa & in each treatment EA<- colSums(b1[3:12]) sortedEA<- EA[order(-EA)]

EB<- colSums(b2[3:12]) sortedEB<- EB[order(-EB)]

LA<- colSums(b3[3:12], na.rm=TRUE) sortedLA<- LA[order(-LA)]

LB<- colSums(b4[3:12]) sortedLB<- LB[order(-LB)]

#plot the number of stems in each taxa and treatment par (mfrow=c(2,1)) #par(c(5, 4, 4, 2) + 0.1, mgp = c(4, 1, 0)) barplot(sortedEA, ylim=c(0,100), ylab="# of Flowering Stems", las=2, main="Early Annual") barplot(sortedEB, ylim=c(0,100), ylab="# of Flowering Stems", xlab="Asteraceae Species", las=2, main="Early Biennial")

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par (mfrow=c(2,1)) par(mgp = c(5, 1, 0)) barplot(sortedLA, ylim=c(0,2500), ylab="# of Flowering Stems", las=2, main="Late Annual") barplot(sortedLB, ylim=c(0,2500), ylab="# of Flowering Stems", xlab="Asteraceae Species", las=2, main="Late Biennial") par (mfrow=c(1, 1))

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Part G: PollinatorVisitors.R

# # Pollinator R analysis - Undergraduate Thesis, 2015 # # 9 April 2015 # # INITIALIZE WORKING DIRECTORY AND CLEAR OUT OLD DATA # set the working directory, and check that working directory is correct rm(list=ls()) ls() setwd('/Users/juliejung/Desktop/SR YR/Thesis') getwd() # check this to see that you have the correct directory!

# READ THE DATA INTO A DATA FRAME AND CHECK THE DATA # First Read in and extract the desired data ########################## allplots.df<-read.csv("ALLplots.csv") # read the data into a data frame allplots.df # type out the data frame contents

# Check the contents of the data frame ########################## summary(allplots.df) # quick summaries of contents # note difference between # qualitative variables (= factors) # quantitative variables (= numerical vars.) head(allplots.df) # list only top or bottom rows of data frame tail(allplots.df)

########################## # Determine Visitor Counts to Each of the Points in each of the Plots ########################## visitorcount = table(allplots.df$PlotPoint) visitorcount #works because each new row is one new visitor A1PtA A1PtC A1PtD A2PtA A2PtB A2PtC A2PtD A3PtB A3PtC A3PtD A4PtA 13 32 27 3 7 30 186 3 10 124 43 A4PtC A4PtD B1PtA B1PtC B1PtD B2PtA B2PtD B3PtA B3PtB B3PtC B3PtD 11 27 5 2 80 3 7 25 7 82 76 B4PtA B4PtB B4PtC B4PtD C1PtA C1PtB C1PtC C2PtA C2PtB C2PtC C3PtA 7 38 155 146 4 29 36 6 2 9 51 C3PtB C3PtC C4PtA C4PtB D1PtA D2PtA D2PtB D2PtC D2PtD D3PtA D3PtC

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84 111 13 26 1 11 14 48 35 4 9 D3PtD D4PtA D4PtB D4PtC D4PtD 29 106 87 60 8

# Read in the Data from Strata Data File ########################## boxplotpollinators.df<-read.csv("BoxplotPollinatorsHMF.csv") # read the data into a data frame boxplotpollinators.df # type out the data frame contents summary(boxplotpollinators.df) # quick summaries of contents Block Point Treatment Visitors lnVisitors A1 : 4 A:16 EA:16 Min. : 0.0 Min. :0.000 A2 : 4 B:16 EB:16 1st Qu.: 2.0 1st Qu.:1.099 A3 : 4 C:16 LA:16 Median : 10.5 Median :2.441 A4 : 4 D:16 LB:16 Mean : 31.1 Mean :2.387 B1 : 4 3rd Qu.: 37.5 3rd Qu.:3.650 B2 : 4 Max. :186.0 Max. :5.231 (Other):40 NA's :2 NA's :2 #treatment <- boxplotpollinators.df[,3] #visitors <- boxplotpollinators.df[,4] mean = aggregate(boxplotpollinators.df$Visitors, list(boxplotpollinators.df$Treatment), mean, na.rm=TRUE) mean Group.1 x 1 EA 8.133333 2 EB 23.250000 3 LA 40.000000 4 LB 52.125000 SD = aggregate(boxplotpollinators.df$Visitors, list(boxplotpollinators.df$Treatment), sd, na.rm=TRUE) SD Group.1 x 1 EA 11.44469 2 EB 47.88389 3 LA 47.44621 4 LB 44.44753 median = aggregate(boxplotpollinators.df$Visitors, list(boxplotpollinators.df$Treatment), median, na.rm=TRUE) median Group.1 x 1 EA 1.0 2 EB 5.5 3 LA 29.0

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4 LB 55.5 # Notched boxplot of Number of Visitors against two crossed factors (Treatment Time and Treatment Frequency) # boxes colored for ease of interpretation

#ln(Visitors) boxplot(lnVisitors~Treatment, data=boxplotpollinators.df, notch=TRUE, col=(c("gold","darkgreen")), xaxt="n", xlab="Treatment", ylab="Natural Log of the Number of Visitors") legend("topleft", inset=.02, title="Treatment Frequency", c("Annual","Biennial"), fill=(c("gold","darkgreen")), horiz=TRUE, cex=0.7) axis(1, at=(c(1.5, 3.5)), labels=(c("Early", "Late"))) abline(v=c(2.5),lty=2, col="blue")

Tukey multiple comparisons of means for richness (S) 95% family-wise confidence level

Fit: aov(formula = H ~ treatment, data = Final_Div_Vals.df)

$treatment diff lwr upr p adj EB-EA 0.34028608 -0.18610915 0.8666813 0.3284762

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LA-EA 0.62376444 0.09736921 1.1501597 0.0139349 LB-EA 0.60262688 0.07623165 1.1290221 0.0186590 LA-EB 0.28347836 -0.24291687 0.8098736 0.4901702 LB-EB 0.26234080 -0.26405443 0.7887360 0.5559196 LB-LA -0.02113756 -0.54753279 0.5052577 0.9995675

#log10(Visitors+1) boxplot(log10(Visitors+1)~Treatment, data=boxplotpollinators.df, notch=TRUE, col=(c("gold","darkgreen")), xaxt="n", xlab="Treatment", ylab="Log10 of the Number of Visitors") legend("topleft", inset=.02, title="Treatment Frequency", c("Annual","Biennial"), fill=(c("gold","darkgreen")), horiz=TRUE, cex=0.65) axis(1, at=(c(1.5, 3.5)), labels=(c("Early", "Late"))) abline(v=c(2.5),lty=2, col="blue")

boxplot(Visitors~Treatment, data=boxplotpollinators.df, notch=TRUE, col=(c("gold","darkgreen")), xaxt="n", main="Number of Visitors Against 2 Crossed Treatment Factors", xlab="Treatment", ylab="Number of Visitors") legend("topleft", inset=.02, title="Treatment Frequency", c("Annual","Biennial"), fill=(c("gold","darkgreen")), horiz=TRUE, cex=0.7) axis(1, at=(c(1.5, 3.5)), labels=(c("Early", "Late"))) abline(v=c(2.5),lty=2, col="blue")

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# CAPTION: "The figure shows a notched boxplot of the number of visitors to plots of each treatment, separated by two factors: time (early or late) and frequency (annual or biennial). # The bold horizontal bar indicates the median (?) number of visitors; the notches show the upper and lower quartiles; the arrows show the minimum and maximum observations. If two boxes' notches (where starts to taper) do not overlap this is ‘strong evidence’ their medians differ at approximately the 0.95 confidence interval (Chambers et al., 1983, p. 62). #statmethods.net/graphs/boxplot.html

############### Anova stats #######################################

#boxplotpollinators.df #group by block readyforanova.df <- aggregate(boxplotpollinators.df$Visitors, list(boxplotpollinators.df$Block), mean, na.rm=TRUE) readyforanova.df Group.1 x

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1 A1 24.00000 2 A2 56.50000 3 A3 34.25000 4 A4 20.25000 5 B1 21.75000 6 B2 2.50000 7 B3 47.50000 8 B4 86.50000 9 C1 21.66667 10 C2 4.25000 11 C3 61.50000 12 C4 9.75000 13 D1 0.25000 14 D2 27.00000 15 D3 10.50000 16 D4 65.25000

Visitors <-aov(x ~ Group.1, data = readyforanova.df) Visitors Call: aov(formula = x ~ Group.1, data = readyforanova.df)

Terms: Group.1 Sum of Squares 9814.632 Deg. of Freedom 15

Estimated effects may be unbalanced summary(Visitors) Df Sum Sq Mean Sq Group.1 15 9815 654.3

#Ln or Log10 boxplotpollinators.df Block Point Treatment Visitors lnVisitors 1 A1 A EA 13 2.6390573 2 A1 B EA NA NA 3 A1 C EA 32 3.4965076 4 A1 D EA 27 3.3322045 5 A2 A EB 3 1.3862944 6 A2 B EB 7 2.0794415 7 A2 C EB 30 3.4339872 8 A2 D EB 186 5.2311086

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9 A3 A LB 0 0.0000000 10 A3 B LB 3 1.3862944 11 A3 C LB 10 2.3978953 12 A3 D LB 124 4.8283137 13 A4 A LA 43 3.7841896 14 A4 B LA 0 0.0000000 15 A4 C LA 11 2.4849067 16 A4 D LA 27 3.3322045 17 B1 A EB 5 1.7917595 18 B1 B EB 0 0.0000000 19 B1 C EB 2 1.0986123 20 B1 D EB 80 4.3944492 21 B2 A EA 3 1.3862944 22 B2 B EA 0 0.0000000 23 B2 C EA 0 0.0000000 24 B2 D EA 7 2.0794415 25 B3 A LB 25 3.2580965 26 B3 B LB 7 2.0794415 27 B3 C LB 82 4.4188406 28 B3 D LB 76 4.3438054 29 B4 A LA 7 2.0794415 30 B4 B LA 38 3.6635616 31 B4 C LA 155 5.0498560 32 B4 D LA 146 4.9904326 33 C1 A LA NA NA 34 C1 B LA 29 3.4011974 35 C1 C LA 36 3.6109179 36 C1 D LA 0 0.0000000 37 C2 A EB 6 1.9459101 38 C2 B EB 2 1.0986123 39 C2 C EB 9 2.3025851 40 C2 D EB 0 0.0000000 41 C3 A LB 51 3.9512437 42 C3 B LB 84 4.4426513 43 C3 C LB 111 4.7184989 44 C3 D LB 0 0.0000000 45 C4 A EA 13 2.6390573 46 C4 B EA 26 3.2958369 47 C4 C EA 0 0.0000000 48 C4 D EA 0 0.0000000 49 D1 A EA 1 0.6931472 50 D1 B EA 0 0.0000000 51 D1 C EA 0 0.0000000 52 D1 D EA 0 0.0000000

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53 D2 A LA 11 2.4849067 54 D2 B LA 14 2.7080502 55 D2 C LA 48 3.8918203 56 D2 D LA 35 3.5835189 57 D3 A EB 4 1.6094379 58 D3 B EB 0 0.0000000 59 D3 C EB 9 2.3025851 60 D3 D EB 29 3.4011974 61 D4 A LB 106 4.6728288 62 D4 B LB 87 4.4773368 63 D4 C LB 60 4.1108739 64 D4 D LB 8 2.1972246 readyforanovaln.df <- aggregate(boxplotpollinators.df$lnVisitors, list(boxplotpollinators.df$Block, boxplotpollinators.df$Treatment), mean, na.rm=TRUE) readyforanovaln.df Group.1 Group.2 x 1 A1 EA 3.1559231 2 B2 EA 0.8664340 3 C4 EA 1.4837235 4 D1 EA 0.1732868 5 A2 EB 3.0327079 6 B1 EB 1.8212052 7 C2 EB 1.3367769 8 D3 EB 1.8283051 9 A4 LA 2.4003252 10 B4 LA 3.9458229 11 C1 LA 2.3373718 12 D2 LA 3.1670740 13 A3 LB 2.1531258 14 B3 LB 3.5250460 15 C3 LB 3.2780985 16 D4 LB 3.8645660 time<-c("E", "E", "E","E","E","E","E","E", "L","L","L","L","L","L","L","L") freq<-c("A","A","A","A","B","B","B","B","A","A","A","A","B","B","B","B") newdf<-data.frame(cbind(readyforanovaln.df,time,freq)) newdf

Group.1 Group.2 x time freq 1 A1 EA 3.1559231 E A 2 B2 EA 0.8664340 E A 3 C4 EA 1.4837235 E A 4 D1 EA 0.1732868 E A 5 A2 EB 3.0327079 E B

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6 B1 EB 1.8212052 E B 7 C2 EB 1.3367769 E B 8 D3 EB 1.8283051 E B 9 A4 LA 2.4003252 L A 10 B4 LA 3.9458229 L A 11 C1 LA 2.3373718 L A 12 D2 LA 3.1670740 L A 13 A3 LB 2.1531258 L B 14 B3 LB 3.5250460 L B 15 C3 LB 3.2780985 L B 16 D4 LB 3.8645660 L B m_1 <- aov(x ~ 1, data=newdf) m_2 <-aov(x ~ time, data = newdf) m_3 <- aov(x ~ freq, data = newdf) m_4 <-aov(x ~ time + freq, data=newdf) m_5 <-aov(x~ time*freq, data=newdf) > AIC(m_1) #51.4 [1] 51.41292 > AIC(m_2) #44.8 [1] 44.83739 > AIC(m_3) #52.8 [1] 52.79724 > AIC(m_4) #45.8 [1] 45.7703 > AIC(m_5) #47.6 [1] 47.5803 > summary(m_1) #doesn't show pvalue Df Sum Sq Mean Sq F value Pr(>F) Residuals 15 18.14 1.209 > summary(m_2) #pval=0.00708 (lowest AIC value! yay!) Df Sum Sq Mean Sq F value Pr(>F) time 1 7.526 7.526 9.927 0.00708 ** Residuals 14 10.613 0.758 --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 > summary(m_3) #only time is significant Df Sum Sq Mean Sq F value Pr(>F) freq 1 0.685 0.6847 0.549 0.471 Residuals 14 17.453 1.2467 > summary(m_4) #only time is significant Df Sum Sq Mean Sq F value Pr(>F) time 1 7.526 7.526 9.854 0.00783 ** freq 1 0.685 0.685 0.897 0.36097

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Residuals 13 9.928 0.764 --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 > summary(m_5) Df Sum Sq Mean Sq F value Pr(>F) time 1 7.526 7.526 9.205 0.0104 * freq 1 0.685 0.685 0.837 0.3781 time:freq 1 0.117 0.117 0.143 0.7116 Residuals 12 9.811 0.818 --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 > #class(m_1) > #names(m_1) >

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Part H: pollinatordiversity.R

# CALCULATE DIVERSITY INDICES FOR POLLINATOR DATA # APRIL 2015

################################################################# # # SECTION 1: DOWNLOAD AND LOAD PACKAGES # lattice is on disc; get vegan from the Web (see Appendix) DELETE # #################################################################

# INITIALIZE WORKING DIRECTORY AND CLEAR OUT OLD DATA # set the working directory, and check that working directory is correct setwd('/Users/juliejung/Desktop/SR YR/Thesis') getwd() # check this to see that you have the correct directory! rm(list=ls()) # Clear the workspace library(vegan) # Install two packages, vegan (which does standard diversity indeces, H',J and # rarefaction curve) and lattice (program for rapidly visualizing data).Theb load package # vegan library(lattice) # Load package lattice library(nlme) ################################################################# # # SECTION 2: READ IN DATA # These are in R data files and can only be read using load() command in R. # Use summary() or head() to view contents of data frames that are in these files. ################################################################# ########################## allplots.df<-read.csv("ALLplots.csv") # read the data into a data frame allplots.df # type out the data frame contents boxplotpollinators.df<-read.csv("BoxplotPollinatorsHMF.csv") # read the data into a data frame boxplotpollinators.df

Fixed_Div_Vals.df <-read.csv("Fixed_Div_Vals.csv") Fixed_Div_Vals.df

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################################# # Prepare data (I think?) ################################## df <- allplots.df[ ,c(1,4)] df #df to just PlotPoint(1) and Species (4) df$freq <- 1 df$freq df2 <- data.frame(tapply(df$freq, list(df$PlotPoint, df$Species), FUN = sum, na.rm = T)) df2 df2[is.na(df2)] <- 0 # we've created a dataframe of # of visitors of each taxa to each PlotPoint! (df2) df2 colnames(df2) [1] "Ancistrocerus.sp..." "Apis.mellifera" [3] "Augochlorini.1" "Beetle.1" [5] "Beetle.2" "Beetle.3" [7] "Bombus.1" "Bombus.bimaculatus" [9] "Bombus.griseocollis" "Bombus.impatiens" [11] "Bombus.ternarius" "Eristalis.1" [13] "Eristalis.2" "Formicidae.1" [15] "Hesperiidae.1" "Hyperaspis.sp.." [17] "Katydid.1" "Membracidae.1" [19] "Pieris.sp.." "Polistes.sp.." [21] "Specodes.sp..." "Spider.1" [23] "Spilomyia.sp.." "Syrphidae.1" [25] "Syrphidae.2" "Syrphidae.3" [27] "Syrphidae.4" "Syrphidae.5" [29] "Tachnidae.1" "Thomisdae1" [31] "Unknown" "Unknown.1" [33] "Unknown.10" "Unknown.11" [35] "Unknown.12" "Unknown.13" [37] "Unknown.14" "Unknown.15" [39] "Unknown.2" "Unknown.3" [41] "Unknown.4" "Unknown.5." [43] "Unknown.6" "Unknown.7" [45] "Unknown.8" "Unknown.9" [47] "Vespidae.1" "Wasp.1." [49] "Wasp.2" "Wasp.3" [51] "Xylocopa.sp.." rownames(df2) [1] "A1PtA" "A1PtC" "A1PtD" "A2PtA" "A2PtB" "A2PtC" "A2PtD" [8] "A3PtB" "A3PtC" "A3PtD" "A4PtA" "A4PtC" "A4PtD" "B1PtA"

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[15] "B1PtC" "B1PtD" "B2PtA" "B2PtD" "B3PtA" "B3PtB" "B3PtC" [22] "B3PtD" "B4PtA" "B4PtB" "B4PtC" "B4PtD" "C1PtA" "C1PtB" [29] "C1PtC" "C2PtA" "C2PtB" "C2PtC" "C3PtA" "C3PtB" "C3PtC" [36] "C4PtA" "C4PtB" "D1PtA" "D2PtA" "D2PtB" "D2PtC" "D2PtD" [43] "D3PtA" "D3PtC" "D3PtD" "D4PtA" "D4PtB" "D4PtC" "D4PtD" # Reformat the counts for entry into Vegan package # as a matrix (not a data frame). Just run these two lines: df2.Sum.df<-df2[,-1] # remove the first column: BUT WHYY?? colnames(df2.Sum.df) [1] "Apis.mellifera" "Augochlorini.1" [3] "Beetle.1" "Beetle.2" [5] "Beetle.3" "Bombus.1" [7] "Bombus.bimaculatus" "Bombus.griseocollis" [9] "Bombus.impatiens" "Bombus.ternarius" [11] "Eristalis.1" "Eristalis.2" [13] "Formicidae.1" "Hesperiidae.1" [15] "Hyperaspis.sp.." "Katydid.1" [17] "Membracidae.1" "Pieris.sp.." [19] "Polistes.sp.." "Specodes.sp..." [21] "Spider.1" "Spilomyia.sp.." [23] "Syrphidae.1" "Syrphidae.2" [25] "Syrphidae.3" "Syrphidae.4" [27] "Syrphidae.5" "Tachnidae.1" [29] "Thomisdae1" "Unknown" [31] "Unknown.1" "Unknown.10" [33] "Unknown.11" "Unknown.12" [35] "Unknown.13" "Unknown.14" [37] "Unknown.15" "Unknown.2" [39] "Unknown.3" "Unknown.4" [41] "Unknown.5." "Unknown.6" [43] "Unknown.7" "Unknown.8" [45] "Unknown.9" "Vespidae.1" [47] "Wasp.1." "Wasp.2" [49] "Wasp.3" "Xylocopa.sp.." #class(df2.Sum.df) df2Sum<-addmargins(as.matrix(df2.Sum.df), 2) df2Sum # adds up each row ( 2 means across columns) # to make a new variable with the # marginal total of counts across all families # df2Sum is now a matrix (not a data frame) # df2Sum has the plot labels back again!! (How???)

186

################################# # CALCULATE DIVERSITY STATISTICS (S, H, AND J) USING VEGAN PACKAGE FUNCTIONS ##################################

# Each of these commands is "vectorized" = works on all # Site X Treatment Combinations N<-df2Sum[,dim(df2Sum)[2]] H<-diversity(df2.Sum.df, index = "shannon", MARGIN = 1, base = exp(1)) S<-specnumber(df2.Sum.df) J<-H/log(S) # Hab<-levels(df2$row.names)

# Gather estimates into a data frame (Div_Vals.df) Div_Vals.df<-data.frame(N,S,H,J)

# View diversity values in the data frame. This shows the habitat (Hab), number of individuals (N), # family richness (S), Shannon-Weiner Diversity Index (H) and Eveness (J). # This table will be reported in your worksheet.

Div_Vals.df # df with missing 0s and stuff

#Fixed_Div_Vals.df is organized by PlotPoint #Final_Div_Vals.df is sorted by treatment Fixed_Div_Vals.df order(Fixed_Div_Vals.df$treatment) sorted <- with(Fixed_Div_Vals.df, order(treatment, H)) sorted Final_Div_Vals.df <- Fixed_Div_Vals.df[sorted, ] Final_Div_Vals.df summary(Final_Div_Vals.df)

######################################################################## # # To visualize patterns of diversity, plot # of individuals (N), family richness (S), # Shannon-Wiener Diversity Index (H) and Eveness (J) for each treatment # ########################################################################

187 boxplot(H~treatment, data=Final_Div_Vals.df, notch=TRUE, col=(c("gold", "darkgreen")), xaxt="n", ylim=c(-0.5, 1.9), xlab="Treatment", ylab="Shannon-Wiever Diversity Index (H)") legend("topleft", inset=.02, title="Treatment Frequency", c("Annual","Biennial"), fill=(c("gold","darkgreen")), horiz=TRUE, cex=0.65) axis(1, at=(c(1.5, 3.5)), labels=(c("Early", "Late"))) abline(v=c(2.5),lty=2, col="blue")

########### ANOVA (H~Treatment) #####################

> H.df<-aggregate(Fixed_Div_Vals.df$H, list(Fixed_Div_Vals.df$Plot, Fixed_Div_Vals.df$Time, Fixed_Div_Vals.df$Frequency), mean, na.rm=TRUE) > H.df Group.1 Group.2 Group.3 x 1 A1 E A 0.7724304 2 B2 E A 0.5135781 3 C4 E A 0.6596957 4 D1 E A 0.0000000 5 A4 L A 1.1598458

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6 B4 L A 1.2446492 7 C1 L A 0.7784511 8 D2 L A 1.2578157 9 A2 E B 1.1292444 10 B1 E B 0.5962318 11 C2 E B 0.8646389 12 D3 E B 0.7167333 13 A3 L B 0.9694617 14 B3 L B 0.9633079 15 C3 L B 1.0204629 16 D4 L B 1.4029792 > > m_1 <- aov(x ~ 1, data=H.df) > m_2 <-aov(x ~ Group.2, data = H.df) > m_3 <- aov(x ~ Group.3, data = H.df) > m_4 <-aov(x ~ Group.2 + Group.3, data=H.df) > m_5 <-aov(x~ Group.2*Group.3, data=H.df) > AIC(m_1) #14.54466 [1] 14.54466 > AIC(m_2) #7.45 *NOT THE lowest AIC this time!!* [1] 7.448889 > AIC(m_3) #15.62 [1] 15.61836 > AIC(m_4) #7.78 [1] 7.775305 > AIC(m_5) #7.34lowest AIC* WHAT DOES THIS MEAN?????? [1] 7.335846 > summary(m_1) #doesn't show pvalue Df Sum Sq Mean Sq F value Pr(>F) Residuals 15 1.811 0.1207 > summary(m_2) #pval=0.00554 Df Sum Sq Mean Sq F value Pr(>F) Group.2 1 0.7852 0.7852 10.72 0.00554 ** Residuals 14 1.0256 0.0733 --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 > summary(m_3) # freq not signif Df Sum Sq Mean Sq F value Pr(>F) Group.3 1 0.1019 0.1019 0.834 0.376 Residuals 14 1.7089 0.1221 > summary(m_4) # time is signif Df Sum Sq Mean Sq F value Pr(>F) Group.2 1 0.7852 0.7852 11.050 0.00549 ** Group.3 1 0.1019 0.1019 1.433 0.25258

189

Residuals 13 0.9237 0.0711 --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 > summary(m_5) # time is signif Df Sum Sq Mean Sq F value Pr(>F) Group.2 1 0.7852 0.7852 11.880 0.00483 ** Group.3 1 0.1019 0.1019 1.541 0.23817 Group.2:Group.3 1 0.1306 0.1306 1.976 0.18513 Residuals 12 0.7931 0.0661 --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 > summary(m_4) # time is signif summary(m_5) # time is signif

################################################### ###################################################

190

################################################### boxplot(J~treatment, data=Final_Div_Vals.df, notch=TRUE, col=(c("gold", "darkgreen")), xaxt="n", ylim=c(-0.5, 1.1), xlab="Treatment", ylab="Evenness (J)") legend("bottomright", inset=.02, title="Treatment Frequency", c("Annual","Biennial"), fill=(c("gold","darkgreen")), horiz=TRUE, cex=0.65) axis(1, at=(c(1.5, 3.5)), labels=(c("Early", "Late"))) abline(v=c(2.5),lty=2, col="blue")

########### ANOVA (J~Treatment) #####################

> J.df<-aggregate(Fixed_Div_Vals.df$J, list(Fixed_Div_Vals.df$Plot, Fixed_Div_Vals.df$Time, Fixed_Div_Vals.df$Frequency), mean, na.rm=TRUE) > J.df Group.1 Group.2 Group.3 x 1 A1 E A 0.4803595 2 B2 E A 0.4674789 3 C4 E A 0.4153989 4 D1 E A 0.0000000

191

5 A4 L A 0.6472743 6 B4 L A 0.7006281 7 C1 L A 0.5444988 8 D2 L A 0.7591629 9 A2 E B 0.8139397 10 B1 E B 0.5835438 11 C2 E B 0.7106265 12 D3 E B 0.5869339 13 A3 L B 0.6361679 14 B3 L B 0.6922698 15 C3 L B 0.5054459 16 D4 L B 0.7159150 > > m_1 <- aov(x ~ 1, data=J.df) > m_2 <-aov(x ~ Group.2, data = J.df) > m_3 <- aov(x ~ Group.3, data = J.df) > m_4 <-aov(x ~ Group.2 + Group.3, data=J.df) > m_5 <-aov(x~ Group.2*Group.3, data=J.df) > AIC(m_1) #-4.5 [1] -4.516831 > AIC(m_2) #-5.1 *NOT THE lowest AIC this time!!* [1] -5.087884 > AIC(m_3) #-5.5 [1] -5.534571 > AIC(m_4) #-6.7 [1] -6.695297 > AIC(m_5) #-11.4 lowest AIC* WHAT DOES THIS MEAN?????? [1] -11.42914 > summary(m_1) #doesn't show pvalue Df Sum Sq Mean Sq F value Pr(>F) Residuals 15 0.5501 0.03668 > summary(m_2) # time not signif** (for the first time) Df Sum Sq Mean Sq F value Pr(>F) Group.2 1 0.0817 0.08166 2.441 0.141 Residuals 14 0.4685 0.03346 > summary(m_3) # freq not signif Df Sum Sq Mean Sq F value Pr(>F) Group.3 1 0.0946 0.09456 2.906 0.11 Residuals 14 0.4556 0.03254 > summary(m_4) # time is signif Df Sum Sq Mean Sq F value Pr(>F) Group.2 1 0.0817 0.08166 2.839 0.1158 Group.3 1 0.0946 0.09456 3.288 0.0929 . Residuals 13 0.3739 0.02876

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--- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 > summary(m_5) # interaction time:freq is signif!! Df Sum Sq Mean Sq F value Pr(>F) Group.2 1 0.08166 0.08166 3.992 0.0689 . Group.3 1 0.09456 0.09456 4.623 0.0526 . Group.2:Group.3 1 0.12845 0.12845 6.279 0.0276 * Residuals 12 0.24546 0.02046 --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Tukey multiple comparisons of means for Evenness (J) 95% family-wise confidence level

Fit: aov(formula = J ~ treatment, data = Final_Div_Vals.df)

$treatment diff lwr upr p adj EB-EA 0.31023103 -0.01353777 0.6339998 0.0650604 LA-EA 0.29936107 -0.02440773 0.6231299 0.0797935 LB-EA 0.27391973 -0.04984907 0.5976885 0.1252891 LA-EB -0.01086996 -0.32937387 0.3076340 0.9997337 LB-EB -0.03631130 -0.35481522 0.2821926 0.9903894 LB-LA -0.02544134 -0.34394526 0.2930626 0.9966341

> ################################################### ################################################### ################################################### boxplot(S~treatment, data=Final_Div_Vals.df, notch=TRUE, col=(c("gold", "darkgreen")), xaxt="n", ylim=c(-1, 13), xlab="Treatment", ylab="Richness (S)") legend("topleft", inset=.02, title="Treatment Frequency", c("Annual","Biennial"), fill=(c("gold","darkgreen")), horiz=TRUE, cex=0.65) axis(1, at=(c(1.5, 3.5)), labels=(c("Early", "Late"))) abline(v=c(2.5),lty=2, col="blue")

193

> TukeyHSD(S) Tukey multiple comparisons of means 95% family-wise confidence level

Fit: aov(formula = S ~ treatment, data = Final_Div_Vals.df)

$treatment diff lwr upr p adj EB-EA 1.3125 -1.8914434 4.516443 0.7014665 LA-EA 3.3750 0.1710566 6.578943 0.0352126 LB-EA 4.1875 0.9835566 7.391443 0.0054892 LA-EB 2.0625 -1.1414434 5.266443 0.3321506 LB-EB 2.8750 -0.3289434 6.078943 0.0937757 LB-LA 0.8125 -2.3914434 4.016443 0.9079432

########### ANOVA (S~Treatment) #####################

194

> S.df<-aggregate(Fixed_Div_Vals.df$S, list(Fixed_Div_Vals.df$Plot, Fixed_Div_Vals.df$Time, Fixed_Div_Vals.df$Frequency), mean, na.rm=TRUE) > S.df Group.1 Group.2 Group.3 x 1 A1 E A 4.00 2 B2 E A 1.50 3 C4 E A 2.50 4 D1 E A 0.25 5 A4 L A 4.75 6 B4 L A 8.00 7 C1 L A 3.75 8 D2 L A 5.25 9 A2 E B 5.50 10 B1 E B 2.75 11 C2 E B 2.75 12 D3 E B 2.50 13 A3 L B 4.75 14 B3 L B 6.25 15 C3 L B 6.00 16 D4 L B 8.00 > > m_1 <- aov(x ~ 1, data=S.df) > m_2 <-aov(x ~ Group.2, data = S.df) > m_3 <- aov(x ~ Group.3, data = S.df) > m_4 <-aov(x ~ Group.2 + Group.3, data=S.df) > m_5 <-aov(x~ Group.2*Group.3, data=S.df) > AIC(m_1) #73.6 [1] 73.63364 > AIC(m_2) #63.31 *NOT THE lowest AIC this time!!* [1] 63.31122 > AIC(m_3) #74.6 [1] 74.60812 > AIC(m_4) #63.0 lowest AIC* WHAT DOES THIS MEAN?????? [1] 63.00732 > AIC(m_5) #64.9 [1] 64.86954 > summary(m_1) #doesn't show pvalue Df Sum Sq Mean Sq F value Pr(>F) Residuals 15 72.73 4.849 > summary(m_2) # time is signif Df Sum Sq Mean Sq F value Pr(>F) Group.2 1 39.06 39.06 16.24 0.00124 ** Residuals 14 33.67 2.41 ---

195

Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 > summary(m_3) # freq not signif Df Sum Sq Mean Sq F value Pr(>F) Group.3 1 4.52 4.516 0.927 0.352 Residuals 14 68.22 4.873 > summary(m_4) # time is signif Df Sum Sq Mean Sq F value Pr(>F) Group.2 1 39.06 39.06 17.417 0.00109 ** Group.3 1 4.52 4.52 2.013 0.17944 Residuals 13 29.16 2.24 --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 > summary(m_5) # time is signif Df Sum Sq Mean Sq F value Pr(>F) Group.2 1 39.06 39.06 16.216 0.00168 ** Group.3 1 4.52 4.52 1.875 0.19603 Group.2:Group.3 1 0.25 0.25 0.104 0.75288 Residuals 12 28.91 2.41 --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 > ######################################################################## # To visualize patterns of diversity, plot H vs S, H vs N and J vs N for all sites. ########################################################################

# DIVERSITY INDEX PLOT CODE - # set point colors and shapes

AllDivSum.df AllDivSum.df$pch<-c(15, 17, NA, 18, 19) AllDivSum.df$col<-c("blue","red", NA, "purple", "goldenrod")

# Plot some diversity values for all three streams

#par(mfrow=c(3,1)) # habitat (Hab), number of individuals (N), family richness (S), # Shannon-Weiner Diversity Index (H) and Eveness (J) plot(H~S,data=AllDivSum.df,pch=AllDivSum.df$pch,col=AllDivSum.df$col,cex=1.5, ylab="Shannon-Weiner Diversity Index (H)", xlab="Family Richness (S)") legend("bottomright", inset=.08, title=" Treatment: ", legend=text1,cex=0.80,pch=pch1,pt.cex=1.5,col=col1)

196

# more plant you get, more species you add, but species (richness) levels off. Logarithmic plot(H~N,data=AllDivSum.df,pch=AllDivSum.df$pch,col=AllDivSum.df$col,cex=1.5, ylab="Shannon-Weiner Diversity Index (H)", xlab="Number of Individuals (N)") legend("bottomright", inset=.08, title=" Treatment: ", legend=text1,cex=0.80,pch=pch1,pt.cex=1.5,col=col1)

197

plot(J~N,data=AllDivSum.df,pch=AllDivSum.df$pch,col=AllDivSum.df$col,cex=1.5, ylab="Eveness (J)", xlab="Number of Individuals (N)") legend("topright", inset=.08, title=" Treatment: ", legend=text1,cex=0.80,pch=pch1,pt.cex=1.5,col=col1)

198

#plot(H~J,data=AllDivSum.df,type="n",xlab="",ylab="",xaxt="n",yaxt="n") #text1<-c("EA","EB", NA, "LA","LB") #pch1<-AllDivSum.df$pch #col1<-AllDivSum.df$col #legend(0.50,1.4,legend=text1,cex=0.80,pch=pch1,pt.cex=1.5,col=col1) #legend(0.6,1.8,legend="Key:",bty="n") #par(mfrow=c(1,1))

199

################################# # # MAKE RARIFICATION CURVES # THESE CALCULATIONS MADE USING THE Vegan PACKAGE #################################

# RAREFACTION PLOTS # Function that sets to NA the ranks larger than the number in the sample set.NA <- function(vct){ max <- which.max(vct) if(max == length(vct)) return(vct) vct[(max+1):length(vct)] <- NA vct }

# Calculate the RAREFACTION CURVES and prepare them for plotting rarefy.v<-seq(1:max(Div_Vals.df$N)) rarefy.Samples<-rarefy(df2.Sum.df[,],rarefy.v) t.rarefy<-t(rarefy.Samples) t.rarefy2<-apply(t.rarefy,2,set.NA) # set species counts beyond ## sample size to NA t.rarefy2

# set up some plot parameters xmax<-max(N); ymax<-max(S); col.v<-c("blue","red","purple","green") hab.v<-c("EA","EB","LA","LB") N_S.df<-Div_Vals.df[,c("N","S")]

# Now draw the rarefaction curves plot(rarefy.v,t.rarefy2[,9],xlab="Individuals Sampled", ylab="Predicted Rarified Species",type="l",col=col.v[1],xlim=c(0,xmax), ylim=c(0,ymax),lwd=3,main="Rarefaction Curve") points(rarefy.v,t.rarefy2[,10],type="l",col=col.v[2],lwd=3) points(rarefy.v,t.rarefy2[,11],type="l",col=col.v[3],lwd=3) points(rarefy.v,t.rarefy2[,12],type="l",col=col.v[4],lwd=3) points(N_S.df[9:12,"S"]~N_S.df[9:12,"N"],pch=3,col="black",cex=2,lwd=2) legend(2500,2.3,legend=hab.v,lwd=3,col=col.v)

200

Rarefaction curve for pollinators

201

Part I: RankAbundanceVisitors.R

# # Rank Abundance of Pollinator Visitors by Treatment R analysis - Undergraduate Thesis, 2015 # # 27 April 2015 # # INITIALIZE WORKING DIRECTORY AND CLEAR OUT OLD DATA # set the working directory, and check that working directory is correct rm(list=ls()) ls() setwd('/Users/juliejung/Desktop/SR YR/Thesis') getwd() # check this to see that you have the correct directory!

# READ THE DATA INTO A DATA FRAME AND CHECK THE DATA # First Read in and extract the desired data ########################## ########################## allplots.df<-read.csv("ALLplots.csv") # read the data into a data frame allplots.df # type out the data frame contents boxplotpollinators.df<-read.csv("BoxplotPollinatorsHMF.csv") # read the data into a data frame boxplotpollinators.df

InsectTaxa.df <-read.csv("InsectTaxa.csv") InsectTaxa.df

################################# # Prepare data ################################## df <- allplots.df[ ,c(1,4)] df #df to just PlotPoint(1) and Species (4) df$freq <- 1 df$freq df2 <- data.frame(tapply(df$freq, list(df$PlotPoint, df$Species), FUN = sum, na.rm = T)) df2 df2[is.na(df2)] <- 0 # we've created a dataframe of # of visitors of each taxa to each PlotPoint! (df2)

202 df2 colnames(df2) rownames(df2)

# Reformat the counts for entry into Vegan package # as a matrix (not a data frame). Just run these two lines: df2Sum<-addmargins(as.matrix(df2), 2) df2Sum

########################## ##########################

#subset by treatment b1 <- subset(InsectTaxa.df, treatment=="EA", na.rm=TRUE) b2 <- subset(InsectTaxa.df, treatment=="EB", na.rm=TRUE) b3 <- subset(InsectTaxa.df, treatment=="LA", na.rm=TRUE) b4 <- subset(InsectTaxa.df, treatment=="LB", na.rm=TRUE)

EA<- colSums(b1[5:55], na.rm=TRUE) EB<- colSums(b2[5:55], na.rm=TRUE) LA<- colSums(b3[5:55], na.rm=TRUE) LB<- colSums(b4[5:55], na.rm=TRUE)

#par (mfrow=c(2,1)) barplot(EA, ylim=c(0,100), ylab="#Visitors", xlab="Visitor Taxa", las=2, main="Early Annual")

203

barplot(EB, ylim=c(0,200), ylab="#Visitors", xlab="Visitor Taxa", las=2, main="Early Biennial")

204

barplot(LA, ylim=c(0,300), ylab="#Visitors", xlab="Visitor Taxa", las=2, main="Late Annual")

205

barplot(LB, ylim=c(0,400), ylab="#Visitors", xlab="Visitor Taxa", las=2, main="Late Biennial")

206

par (mfrow=c(1, 1))

207

#################################################### #################################################### ## Sort from most common to least common? # Rank order→ point out that there are different dominant species depending on treatment time, #################################################### ####################################################

# find the sum of #visitors in each insect taxa & in each treatment EA<- colSums(b1[5:55], na.rm=TRUE) sortedEA<- EA[order(-EA)] EB<- colSums(b2[5:55], na.rm=TRUE) sortedEB<- EB[order(-EB)] LA<- colSums(b3[5:55], na.rm=TRUE) sortedLA<- LA[order(-LA)] LB<- colSums(b4[5:55], na.rm=TRUE) sortedLB<- LB[order(-LB)]

#par (mfrow=c(2,1)) # mar: c(bottom, left, top, right) = the number of lines of margin to be specified on the four sides of the plot. #The default is c(5, 4, 4, 2) + 0.1

208 barplot(sortedEA, ylim=c(0,100), ylab="#Visitors", xlab="Visitor Taxa", las=2, main="Early Annual", mar=c(1,4,4,2))

209 barplot(sortedEB, ylim=c(0,200), ylab="#Visitors", xlab="Visitor Taxa", las=2, main="Early Biennial")

210

#par (mfrow=c(2,1)) barplot(sortedLA, ylim=c(0,300), ylab="#Visitors", xlab="Visitor Taxa", las=2, main="Late Annual")

211 barplot(sortedLB, ylim=c(0,400), ylab="#Visitors", xlab="Visitor Taxa", las=2, main="Late Biennial") par (mfrow=c(1, 1))

#only show if >0 visitors library(Hmisc) posEA <- subset(sortedEA, sortedEA>0) posEB <- subset(sortedEB, sortedEB>0) posLA <- subset(sortedLA, sortedLA>0) posLB <- subset(sortedLB, sortedLB>0)

212

#same y axes barplot(posEA, ylim=c(0,400), ylab="#Visitors", xlab="Visitor Taxa", las=2, main="Early Annual", cex.axis=0.7, cex=0.7)

213 barplot(posEB, ylim=c(0,400), ylab="#Visitors", xlab="Visitor Taxa", las=2, main="Early Biennial", cex.axis=0.7, cex=0.7)

214 barplot(posLA, ylim=c(0,400), ylab="#Visitors", xlab="Visitor Taxa", las=2, main="Late Annual", cex.axis=0.7, cex=0.7)

215 barplot(posLB, ylim=c(0,400), ylab="#Visitors", xlab="Visitor Taxa", las=2, main="Late Biennial", cex.axis=0.7, cex=0.7)

216

#different y axes barplot(posEA, ylim=c(0,100), ylab="#Visitors", xlab="Visitor Taxa", las=2, main="Early Annual", cex.axis=0.7, cex=0.7)

217 barplot(posEB, ylim=c(0,200), ylab="#Visitors", xlab="Visitor Taxa", las=2, main="Early Biennial", cex.axis=0.7, cex=0.7)

218 barplot(posLA, ylim=c(0,300), ylab="#Visitors", xlab="Visitor Taxa", las=2, main="Late Annual", cex.axis=0.7, cex=0.7)

219 barplot(posLB, ylim=c(0,400), ylab="#Visitors", xlab="Visitor Taxa", las=2, main="Late Biennial", cex.axis=0.7, cex=0.7)

par (mfrow=c(1, 1))

220