Delgado D Doctoralthesis4 2.Pdf (2.939Mb)

DISENTANGLING VINE-INVADED TROPICAL LANDSCAPES: FROM INDIVIDUAL

VINE PATCHES TO VINE NETWORKS

DIANA L. DELGADO RIVERA

A dissertation submitted to the

DEPARTMENT OF BIOLOGY

FACULTY OF NATURAL SCIENCES

UNIVERSITY OF PUERTO RICO

RÍO PIEDRAS CAMPUS

In partial fulfillment of the requirements for the degree of

DOCTOR IN PHILOSOPHY

May 2015

Río Piedras, Puerto Rico

i This dissertation has been accepted by faculty of the: DEPARTMENT OF BIOLOGY

FACULTY OF NATURAL SCIENCES

UNIVERSITY OF PUERTO RICO

RÍO PIEDRAS CAMPUS

In partial fulfillment of the requirements for the degree of

DOCTOR IN PHILOSOPHY

Dissertation Committee:

______Carla Restrepo, Ph.D. Advisor

______Rafael Arce, Ph.D.

______Nick Brokaw, Ph.D.

______Alberto Sabat, Ph.D.

______Jorge Ortiz, Ph.D.

DISENTANGLING VINE-INVADED TROPICAL LANDSCAPES: FROM INDIVIDUAL

VINE PATCHES TO VINE NETWORKS

iii

To my family,

iv ACKNOWLEDGEMENTS

First, I would like to thank God for giving me the strength and persistence to pursue and finish a

PhD degree. I dearly thank my advisor Carla Restrepo for her mentoring and counseling throughout both my undergraduate and graduate studies. I also thank Rafael Arce for all his ideas, questions, and help constructing my complex networks. Special thanks also to the other members of my committee: Nick Brokaw, Jorge Ortiz and Alberto Sabat for their valuable comments and recommendations. I thank D.F. Sax, J. White for reviewing the second chapter of this dissertation and providing useful comments. Thanks to all my fassistants inside and outside the field: Fransheska Odonnell, Fransheska Ruiz-Canino, Rita Caceres, Giomara La Quay, Juan

Carlos Ortega, David Managos, Maria E. Ocasio, and specially Josimar Figueroa, whose hard work and enthusiasm greatly contributed to the completion of the second and third chapters of my dissertations. Thanks also to my other undergraduate assistants: Adriana Sierra and Yakshi

Ortiz, for their help in the generation of the climbing plant database and the pre-processing of satellite images. Thanks to the past and current members of the Tropical Large Scale Ecology lab for all their helpful comments, questions and feedback, especially to Johanna Colón, Jenitza

Melendez, Francheska Lopez, Erick Estela, Katia Ruiz, Kristy Adams, Gerardo Lopez and

Zuania Colón. I would, also like to thank C. Jarnevich from the USGS for her help with the modeling algorithms used for the species distribution models and Eileen Helmer from the USDA

Forest Service for her advice regarding image analysis. I also grateful to all the institutions and programs that help fund my research: the Graduate Biology Program and the Department of

Biology, the Puerto Rico Louis Stokes Alliance for Minority Participation (HRD-0832961), the

Dean of Graduate Studies and Research and the Golf Tournament Fellowship for their financial.

v Finally, I would like to thank my family, and my dear friends who have become part of my extended family. Thank you all for your support and your faith in me. Also thanks for all the coffee breaks, the movie nights, the softball practices, and all the good times I had during this stage of my life.

vi TABLE OF CONTENTS

ACKNOWLEDGEMENTS ...... v

LIST OF TABLES ...... ix

LIST OF FIGURES ...... xi

VINE INVASIONS IN PUERTO RICO ...... 13

FIGURE LEGENDS ...... 6

PREDICTING VINE PROLIFERATING POTENTIAL FROM MULTIPLE TRAITS IN A

DIVERSE TROPICAL INSULAR ASSEMBLAGE ...... 9

INTRODUCTION ...... 10

METHODS ...... 12

RESULTS ...... 17

DISCUSSION ...... 23

TABLES ...... 30

FIGURE LEGENDS ...... 36

ABIOTIC AND BIOTIC VARIABLES IN THE ASSEMBLY OF VINE COMMUNITIES ALONG A

COMPLEX ENVIRONMENTAL GRADIENT ...... 47

INTRODUCTION ...... 48

METHODS ...... 51

RESULTS ...... 60

DISCUSSION ...... 66

TABLES ...... 71

FIGURE LEGENDS ...... 81

INTEGRATING BIOTIC INTERACTIONS TO MODEL SPATIAL NETWORKS OF THE

SPREAD OF A STRUCTURAL PARASITE ...... 94

vii INTRODUCTION ...... 95

INCORPORATING BIOTIC INTERACTIONS TO NETWORKS OF INVASIVE SPREAD- GENERAL

APPROACH TO CREATE COMPLEX WEIGHTED NETWORKS ...... 100

Mikania micrantha IN CENTRAL PUERTO RICO AS A CASE STUDY ...... 101

RESULTS ...... 114

DISCUSSION ...... 119

TABLES ...... 123

FIGURE LEGENDS ...... 134

GENERAL CONCLUSIONS ...... 144

REFERENCES ...... 148

viii LIST OF TABLES

Table 2.1 Vine traits included in the databases with the corresponding sources of information………………………………………………………………………………...…31-32

Table 2.2 Uses given to introduced vine species according to time since first time of collection in

Puerto Rico……………………………….……………………………………………..………..33

Table 2.3 Results from Fisher's exact test of independence between native and alien proliferating species………………………………………………………….……….………………………..34

Table 2.4 Accuracy measures for the Classification Tree and Random Forest Models predicting proliferation potential for vine species based on their traits...... ………………….………..…...35

Table 3.1. Biophysical variables generated for my analyses……………………...……………..72

Table 3.2 Bioclimatic and edaphic variables and their mean values in my study area……...... 73

Table 3.3 The results for the Principal Component Analysis of the selected Bioclimatic variables at the 180m2 neighborhood…………………………………………………………………...... 74

Table 3.4 The results for the Principal Component Analysis of the selected edaphic variables at the 180m2 neighborhood…………………………………………………………………………75

Table 3.5 Vine species found in the sampled vine patches…………………………………..76-77

Table 3.6 Results of stepwise regressions predicting patch vine diversity and eveness based on environmental and land cover variables………………………………………...... 78

Table 3.7 Results from NMDS ordination……………………………………………………79-80

Table 4.1 Rank values calculated for each of the land cover Majority (M) classes used in the

Susceptibility index... ………………………………………………………………………...... 124

ix Rank 4.2 Rank values calculated for each of the land cover Variety (V) classes used in the

Susceptibility index...... 125

Rank 4.3 Rank values calculated for each of the land cover Range (R) classes used in the

Susceptibility index...... 126

Table 4.4 Results from the binomial linear regression model predicting the presence of vine patches based on the influence of land cover variables………………………………………...127

Table 4.5 Results of the modeling of M. micrantha using both Generalized Linear Models and

Generalized Additive Models algorithms…………………………………………………..…..128

Table 4.6 All possible types of nodes present in my networks and their associated value of

Probability of Propagule Establishment ……..…………………………………………………129

Table 4.7 Adjacency matrixes for the host-parasite interactions scenario of the current spread of

Mikania micrantha……………………………………………………………...………………130

Table 4.8 Adjacency matrixes for the host-parasite interactions scenario of the potential spread of

Mikania micrantha……………………………………………………………...………………131

Table 4.9 Characteristics of the different networks of the spread of M. micrantha……………132

Table 4.10 Characteristics of the networks generated after the random or directed elimination of nodes……………………………………………………………………………………………133

x LIST OF FIGURES

Figure 1.1 Vine invaded landscapes around the World……………………..………………….8

Figure 2.1 Climbing species classified according to life form, origin, and proliferation status……...……………………………………………………………………………………...40

Figure 2.2 Vine species classified according to their proliferation status, taxonomic affiliation,

Distribution, Abundance, and intrinsic and extrinsic...………………………………..…...... 41

Figure 2.3 Networks showing the exchange of alien non-proliferating and proliferating vine species reported for Puerto Rico based on their original and current realms of distribution….....42

Figure 2.4 Cumulative distribution of the earliest year of collection of each of the 76 vine species introduced to the island of Puerto Rico.………...... ……………………………………………..43

Figure 2.5 Classification tree showing the proliferation status of Puerto Rican vines based on their attributes.…………………………………………...…………...... ……………………….44

Figure 2.6 Importance of vine traits as predictors of proliferation status in the random forest analyses.………………………………………………………………………………………….45

Figure 3.1. Map of the Caribbean island of Puerto Rico showing the study area and the location of the vine patches visited………………………………………………………………………..85

Figure 3.2 Relative abundance and distribution of vine species per vine patch…...... ………...86

Figure 3.3 Distribution of values of Shannon - Weaver diversity index, and Shannon’s evenness index along the vine patches sampled……………………………………………………………87

Figure 3.4 Heatmap showing the vine species abundance throughout all vine patches.….……..88

xi Figure 3.5 The non-metric multidimensional scaling ordination of sites and vine abundance at the small (180 m2) scale……………………………………………...... ………………………...... 89

Figure 3.6 The non-metric multidimensional scaling ordination of sites and vine density at the small (180 m2) scale……………………………………………………………………………...90

Figure 3.7 Spatial distribution of the scores of the first and second axis of the ordination…..….91

Figure 3.8 The non-metric multidimensional scaling ordination of sites and vine abundance at the medium and large scale…………………………………………………………………………..92

Figure 3.9 The non-metric multidimensional scaling ordination of sites and vine density at the medium, and large scale………………………………………………………………………….93

Figure 4.1 Diagram explaining the construction of a complex network………………..………137

Figure 4.2 Maps showing the spatial distribution of the coarse and fine scale networks of the actual and potential spread of M. micrantha……………………………………..……………..138

Figure 4.3 Maps showing the spatial distribution of the coarse and fine scale networks of the actual and potential spread of M. micrantha under the high virulence scenario...……………..139

Figure 4.4 Maps showing the spatial distribution of the twelve spread networks of M. micrantha constructed ……………………………………………………………………………………..140

Figure 4.5 Scatter plots showing the degree distribution of nodes in each of the three biotic interactions networks…………………………………………………………………………...141

Figure 4.6 Scatter plots showing the degree distribution of nodes in each of the three biotic interactions networks under the high virulence scenario…..…………………………………...142

Figure 4.7 Bar plots comparing the number of edges, components and size of the core component of the networks after removing 10% of the nodes through a random and directed process………………………………………………………………………………………..…143

xii

CHAPTER ONE

VINE INVASIONS IN PUERTO RICO

xiii Vines, like their woody counterparts, lianas, are increasing in abundance in several parts of the

World (Warshauer et al. 1983, Mackey et al. 1996) including both tropical (Shrestha and Dangol

2014) and temperate regions (Figure 1.1; Miller 1997). Vines can smother vegetation

(McGaughey 2011) and human-made structures alike (Miller 1997) by forming extensive patches that resemble dense green blankets that cover vast areas. Vine invasions are often attributed to individual species of alien origin (Warshauer et al. 1983, Mackey et al. 1996, Miller

1997). In Puerto Rico it is very common to find extensive vine patches and based on work conducted elsewhere, I was expecting that few introduced vine species were involved in these vine invasions. For sure I knew that vine proliferation had become a recurrent and costly problem for the Puerto Rico’s Electric Power Authority -PREPA- and that it even captured the attention of the Puerto Rican Senate (e.g., Resolución del Senado # 1696, 2010). Soon it became clear that vine patches were composed of multiple vine species, both of native and alien in origin, and that they were altering the configuration of the landscape. This fueled my interest to characterize the structure and composition of vines patches, investigate the factors influencing their composition and examine possible ways in which vine patches organize in complex networks connecting vine communities in a landscape. Also, I wanted to examine how the different types of biotic interactions taking place within vine communities could regulate their spread and overall invasive success.

The invasive success of vines most likely reflects 1) their intrinsic characteristics, 2) the prevailing conditions in the places where they have established (Lake and Leishman 2004), and

3) the type of biotic interactions that they develop with other species in the invaded area (Belote and Weltzin 2006). Here I examine these three aspects to gain a better understanding of the dynamics behind the observed vine invasions. In order to understand the relationship between

2 intrinsic and extrinsic vine traits and their proliferation status, in chapter two I compiled an extensive database to characterize Puerto Rico’s contemporary vine assemblage and establish relationships between vine proliferation status and plant traits. I examined the association between vine origin and proliferation status with taxonomy, distribution, intrinsic, and extrinsic traits in order to understand the aggressive proliferation of vines, indistinct of their origin. Then,

I used a suite of traits, both intrinsic and extrinsic that can help predict proliferation status in vines. Results showed that vine origin was associated only to one of seven of the traits examined, which allow the grouping of alien and native proliferating species together when analyzing which proliferating status was associated with the various traits. In addition, we were able to successfully predict vine proliferation status based on five of traits, namely fruit type, use, abundance, distribution, and seed dispersal mode.

In chapter three I focused on determining which abiotic and biotic variables influence both vine diversity and species composition in vine communities. For this I went to the field and measured the abundance of each vine species present in a subset of vine patches found along a complex mosaic of environmental and land cover variables. Results showed that both vine diversity and species composition within vine communities can be explained by a combination of composite climatic, edaphic, topographic and land cover variables. In particular, climatic variables such as those related to extreme temperatures and precipitation, appeared to be the most important influencing vine diversity within vine patches.

In terms of species interactions vines represents a particular example of host - parasite interactions because they, like all climbing plants, are structural parasites that need of other structures for physical support (Forseth and Innis 2004). In consequence, host availability and diversity most likely influences the structure of vine communities (Nabe-Nielsen 2001).

3 Nevertheless, most studies that focus on climbing plant communities -principally working with lianas- (with the exception of Molina-Freaner et al. 2004) examine separately the influence of abiotic variables (Molina-Freaner et al. 2004, DeWalt et al. 2006, Swaine and Grace 2007,

DeWalt et al. 2010) and that of climbing plants hosts’ preference (DeWalt et al. 2000, Nabe-

Nielsen 2001, Carrasco-Urra and Gianoli 2009, Leicht-Young et al. 2010). Highlighting the need to incorporate together the influence of abiotic and biotic interaction in the study of vine communities in order to get a better understanding of what may be influencing their current expansion.

I propose the use of graph theory as a way to incorporate both abiotic and biotic interactions in the modeling of vine invasions. Graphs or networks are simple representations of complex systems whose structure, connectivity, and function can be described through a series of metrics

(Urban and Keitt 2001, Minor and Urban 2008). Applying this approach to vine spread can help untangle the dynamic and complex processes behind these invasions and design management plans for vine-invaded landscapes (Ferrari et al. 2014, Stewart-Koster et al. 2015). Among other things, this type of approach can help predict and prevent further vine invasions (Sutrave et al.

2012, Stewart-Koster et al. 2015). In consequence, in chapter four, I used a graph theory approach to model the connectivity of structural hosts of the most common and prolific vine in my study area, Mikania micrantha. I also modeled the effect of different biotic interactions among vine species in the size, structure and connectivity of these host-parasite networks.

Through the creation of a series of networks that varied in terms of scale and biotic interactions between Mikania micrantha, its hosts and other vine species present in the vine patches (e.g.,

Pueraria phaseoloides), I showed that competition between M. micrantha and P. phaseoloides

4 had little effect constraining the size, connectivity and structure of the network of M. micrantha’s.

Finally, in chapter five I revisit the most important findings of my dissertation and how they have help gain a better understanding of vine invasions. I also highlight the applicability of the approaches used in my research, such as the use of networks to understand the dynamics of vine spread. This approach can easily be applied to other vine species as well as other host-parasite systems and help inform management and conservation plans.

CHAPTER ONE

FIGURE LEGENDS

6 Figure 1.1 Vine invaded landscapes around the World. The map shows seven examples of vine invasions and the respective invasive vine species. a) Pueraria montana in the Southeastern cost of the United States, b) Mikania micrantha in Southern China, c) Merremia peltata in several

Pacific Islands, d) Ipomoea spp. in Puerto Rico, e) Antigonon leptopus in St. Eustatius and f)

Cardiospermum grandiflorum in Africa, and f) Cryptostegia grandiflora in Australia.

7 FIGURE 1.1

CHAPTER TWO

PREDICTING VINE PROLIFERATING POTENTIAL FROM MULTIPLE TRAITS IN A

DIVERSE TROPICAL INSULAR ASSEMBLAGE

9 INTRODUCTION

Vines with their herbaceous and climbing habits represent a diverse functional group of plants and an important component of many habitats and successional communities (Gentry 1991, Bush et al. 1995). In many regions around the globe an increasing presence of alien vines, as well as a proliferation of numerous native and alien vine species, is not only changing the composition of vine assemblages and plant communities but also of ecosystem functions (Hegarty and Caballé

1991, Meyer and Lavergne 2004, Harris et al. 2007, Hickman and Lerdau 2013). Vine cover can increase rapidly in fragmented habitats and abandoned agricultural lands, smothering plant canopies, forest edges, and infrastructure alike over vast areas (e.g., Mackey et al. 1996,

Blaustein 2001, Kirkham 2005). What traits make some vine species more susceptible than others to proliferate remains unknown, yet addressing this question may prove important for understanding the processes underlying the reorganization of plant assemblages and managing vine-dominated landscapes.

Vine traits, including climbing mechanisms, are highly variable which may explain vines’ success at exploiting resources that change in space and time (Lambert and Arnason 1986, Bush et al. 1995, Oliver 1996, McNab and Loftis 2002, Gallagher and Leishman 2012). Likewise, vines have diverse life-history strategies, including patterns of carbon allocation, that have served as the basis for identifying cover crops suitable for different environmental conditions

(Hairiah and van Noordwijk 1989, Kolawole and Kang 1997). Not surprisingly, a subset of the alien vines proliferating in many regions have high horticultural value, thus the use given to the species may explain their success in human-dominated environments (Pyšek et al. 2009, Harris and Gallagher 2011). However, not all proliferating alien and native species have a high

10 horticultural value and this raises questions about the suites of traits that characterize different groups of vines in regional assemblages.

Identifying suites of traits associated with the proliferation of vines is also important for conservation and management purposes. Proliferating vines not only can cover extensive areas but also can potentially change ecosystem and landscape-level processes. Vines may alter species composition through their effect on demographic processes (Zhang et al. 2011), nutrient and water fluxes (Lambert and Arnason 1986, Lehmann et al. 1999), and disturbance regimes including patterns of land use (Cochrane and Schulze 1999, Douglass et al. 2009). Finally, proliferating vines can alter ecosystem functions, landscape structure, composition, and connectivity (Savage 1992, Mackey et al. 1996, Ogle et al. 2000, Hickman and Lerdau 2013).

Thus, identifying key traits associated with the proliferation of vines is crucial to prevent the introduction of species with similar potential, monitor vine species likely to proliferate in the future, and manage vine-dominated landscapes.

At least three approaches that incorporate plant traits have been used to assess the potential of climbing species to proliferate; these focus on introduced species that may become invasive under changing environmental conditions. The first, centered on Australia's climbing plant assemblages, integrates information of three traits directly linked to the introduction phase of the invasion processes (Harris et al. 2007). The second approach focuses on all introduced species and integrates information on their history, biogeography, biology, ecology, and presence of undesirable traits to provide a semi-quantitative assessment of the risk for becoming invasive

(Pheloung 1995, Daehler and Carino 2000, Werren 2001). Here a climbing or smothering growth habit is listed as an undesirable trait. Thus, climbing plants may be predisposed to be classified as potential invaders when they are not. The third approach, focusing on one climbing

11 plant species, integrates key demographic traits such as propagule availability and local abundance with bioclimatic models to forecast its spatial and temporal spread in North America

(Ibáñez et al. 2014). This approach relies on demographic data linked to the various phases of ongoing invasion processes, thus for diverse and little known vine assemblages this approach may be difficult to implement.

Here I propose a fourth approach to understand the proliferation of vines while overcoming some of the limitations listed above. Focusing on an insular region that like many others may be highly susceptible to invasions (Lonsdale 1999), I set up to assemble an extensive database aimed at characterizing a regional vine assemblage and establishing relationships between vine proliferation status and plant traits. By proliferation status I refer to vine’s capacity to smother plant canopies, forest edges, and infrastructure alike; thus including under this term both alien invasive and native encroaching species. Centering on the tropical island of Puerto Rico, I asked three questions: 1) What is the contribution of alien species to a contemporary insular vine assemblage? 2) Is vine origin and proliferation status associated with taxonomy, distribution, intrinsic, and extrinsic traits?, and 3) Can these traits predict their proliferation?

METHODS

Study area

Puerto Rico is the smallest and easternmost island of the Greater Antilles (18° 15’N, 66° 30’W;

9,104 km²). The island encompasses diverse life zones as well as land uses (Ewel and Whitmore

1973, Rosenberry et al. 1995, Helmer et al. 2008). In the early to mid-1900's agriculture (85%), forest (20%), and urban (<2%) covered Puerto Rican landscapes. Today, these figures have drastically changed [agriculture (38%), forest of different ages (45%), and urban (15%)]

12 evidencing a marked shift from agriculture to other economic activities. The dynamics of land use change coupled with a dense network of public infrastructure developed over the years has created numerous habitats amply used by native and non-native vines. Although proliferating vines are observed both in urban and rural environments, it is in the latter where their impact might be greatest. For example, a preliminary study conducted in north-central Puerto Rico, showed that 49.4 km2 or 3.0% of the area was covered by vine patches and 58% of the utility poles by vines (Delgado et al. in prep.).

Construction of climbing plant databases

I compiled a list of Puerto Rican climbing plants based initially on Acevedo-Rodriguez (2005) but subsequently updated using Axelrod (2011). For all species I obtained information on life form, origin, and proliferation status. Climbing species were classified into lianas (woody climbing plants with thick stems growing in mature forests) and vines (non-woody and sub- woody climbing species, including shrubs, with thin stems often growing in disturbed areas and forest edges) (Gentry 1991, Axelrod 2011). Species were further classified by origin into alien

(species introduced accidentally or intentionally as a result of human activity) or native (species whose historical distribution included Puerto Rico), and by proliferation status into proliferating species (including alien species producing large numbers of reproductive offspring and spreading into new areas, and native encroaching or weedy vines) (Richardson et al. 2000) and non- proliferating species. I used a list of Puerto Rican weeds (Más and Lugo-Torres 2013), information provided by an expert on Puerto Rican plants (E. Santiago, per. com.), and my own observations (D. Delgado, unpublished data) to determine the proliferation status of the species in Puerto Rico.

13 I focused next on the subset of climbing species classified as vines for two reasons. First, the vast majority of climbing plants reported for the island are vines. Second, most if not all known climbing species that are proliferating in the island belong to this life form. I ran an extensive literature review and herbaria search to collect information on species' distribution, intrinsic and extrinsic traits, and potential for proliferation in Puerto Rico based on their invasive status elsewhere (Table 2.1; Appendix 1). A species with a potential for proliferation was any species in my database listed in World and Regional lists as invasive or weedy (Appendix 1).

Distribution, abundance, and biogeographic origin

Four complimentary approaches were used to characterize the distribution of vines in space and time (Table 2.1). First based on the extent of their distribution vines were classified into endemic (only recorded in the island), native (recorded in Puerto Rico, the Caribbean, and parts of the Neotropics), casual alien (not part of the original flora of Puerto Rico, and a specie that has escaped cultivation), and naturalized alien (introduced to the island with self-sustaining populations; Richardson et al. 2000, Acevedo-Rodriguez 2005). Second, based on their occurrence in biographic realms I characterized the original and current biogeographic distribution of vines (Olson et al. 2001; Table 2.1). Third, a search in the Universidad de Puerto

Rico [UPR] and Universidad de Puerto Rico-Mayaguez campus [MAPR] herbaria allowed us to obtain information on the earliest year of collection for 92% (70 species) of the alien vine species. UPR and MAPR are the largest herbaria in the island, and MAPR has the oldest records, dating back to the 1880’s (Table 2.1). Lastly, species were assigned to one of three local abundance categories derived from a semi-quantitative measure of abundance derived from

Axelrod (2011): common (species found in ≥ 5 of 14 geographic regions), uncommon (species

14 found in 1 to 4 geographic regions or ≥ 3 of 24 designated state forest reserves, or ≥ 3 of 78 municipalities), and rare (species reported in < 3 state forest reserves or < 3 municipalities).

Thus, this definition of local abundance describes the distribution and not the population size of any given species within the island.

Intrinsic and extrinsic traits

I focused on four intrinsic (life span, climbing mechanism, fruit type, and dispersal mode) and one extrinsic (use) traits that are relevant to understand the proliferation of vines, and for which information for most species could be obtained (Binggeli 1996, Thuiller et al. 2006). Vine species were assigned to either of three life span classes, namely annual, perennial or annual/perennial (Harper 1977). The annual/perennial class included species that can act either as annuals or perennials depending on climatic or geographical conditions. I also classified vine species according to six climbing mechanism categories (Putz and Mooney 1991, Acevedo-

Rodriguez 2005). Three of these (aerial roots, tendrils, twining) represent active climbing mechanisms that involve specialized structures to climb, attach, or twist onto other structures.

The other three (scandent, sarmentose, and spines) are passive climbing mechanisms that involve structures or habits not evolved for climbing but that allow the plants to lean or attach stem runners on other structures. Vines were also assigned to four general and 25 specific fruit types

(Spjut 1994; Table 2.1). The general categories included simple (indehiscent fruits derived from one flower and one carpel), schizocarpic (fruits derived from one flower and two or more carpels; instead of dehiscing, fruits split among numerous segments), rhexocarpic (dehiscent fruits derived from one flower and two or more carpels; seeds are shed through sutures or openings of the pericarp), and compound fruits (fruits derived from more than one flower). For

15 8% of the species I had to use genus level information. The last intrinsic trait used to characterize the vine species was seed dispersal mode and included four categories: anemochory

(dispersal by wind), hydrochory (dispersal by water), autochory (dispersal by gravity), and zoochory (dispersal by animals either by endozoochory or exozoochory) or a combination of these deduced from fruit type or from published accounts (van der Pijl 1972; Appendix 1). Use, my extrinsic trait, included four categories: handicraft, ornamental, horticultural, and medicinal or a combination of these if more than one use was reported (Table 2.1).

Data analysis

To test for associations between pairs of categorical variables I used Fisher’s exact and chi- square independence tests with Yate’s correction (Ott and Longnecker 2010). Analyses of the residuals allowed us to determine which cells made the largest contribution to the chi-square results (Ott and Longnecker 2010). A log-linear model was used to test for interactions in a three-way contingency table including life form, origin and proliferation status. To visualize the nature of vine exchange among realms I generated two networks, one for non-proliferating alien and the other for proliferating alien species (Gephi 0.8.2). I used as input two matrices in which rows and columns depicted the original and current realms of distribution, respectively, of these two groups of vines. In these networks, the nodes represent the biogeographic realms and arrows the exchange among them. The nodes were weighted using the number of species original to each realm. To test for differences in residence time between proliferating and non-proliferating species I used a Student t–test.

In order to predict vine proliferation status based on the traits listed in Table 2.1, I ran a

Classification Tree model followed by a Random Forest analysis. A classification tree model

16 partitions the data into groups, with the objective of reducing within group heterogeneity

(McCune and Grace 2002). The product of this recursive partitioning was a decision tree that predicted vine proliferation status based on a group of chosen predictors or vine traits (Table

2.1). The resulting tree model was pruned to its optimal size by reducing the number of nodes in order to minimize both classification error and tree complexity. Because in datasets like mine in which phylogenic relationships may create dependencies among the observations, running a random forest analysis may help explore further the importance of the predictors under conditions that reduce, if not eliminate these dependencies (Cutler et al. 2007, Davison et al.

2009). A random forest analysis grows multiple classification trees from subsets of observations drawn at random with replacement and by selecting subsets of predictor variables at each node to create a mean, modal tree (Cutler et al. 2007). Thus a combination of multiple independent classification trees, 5000 in my case, allowed me to determine the relative importance of each of the traits used as predictors of proliferation status. Both approaches use a cross-validation procedure to examine model accuracy (Cohen's Kappa, Percentage of Correctly Classified observation [PCC], Sensitivity, and Specificity). All statistical analyses were run in R version

2.14.2. I used the tree (Ripley 2014), randomForest (Liaw and Wiener 2002), and fmsb

(Nakazawa 2014) R packages to fit the tree model, run the random forests, and calculate Cohen's

Kappa, respectively.

RESULTS

Puerto Rico has a total of 313 climbing species in 52 families and 161 genera. Of these, 80%

(267 species) correspond to vines, and the remaining 15% percent (46 species) to lianas. The log-

17 linear analysis showed two two-way interactions between life form and proliferation status, and origin and proliferation status (G2 = 9.99, df = 2, P = 0.01). In order to understand the nature of the interaction I ran chi-squared analyses on two two-way contingency tables which showed that while origin and proliferation status were independent (χ2 = 2.508, df = 1, P = 0.11; Fig. 2.1), life form and proliferation status were not independent, with lianas having fewer proliferating species than expected by chance (χ2 = 6.298, df = 1, P = 0.01; Fig. 2.1). Based on these results the remaining analyses focus on vines.

Composition of an insular vine assemblage

The 267 species of vines are in 126 genera and 42 families with 55% of all the species belonging to 5 families (Fabaceae, Convolvulaceae, Cucurbitaceae, Apocynaceae, and Asteraceae; Fig.

2.2a). Distinguishing species by origin revealed that 76 or 28% of the recorded species were alien and with the exception of 9 species in 8 genera and 8 families, they belong to families already present in the island. Among alien vines, 27 or 36% of the species in 13 families are proliferating in the island whereas among native vines 53 or 28% of the species in 15 families have a similar status. Three of the 13 families with alien proliferating vines (3 species) are not represented among their native counterpart. Thus, the majority of alien vines in the island are taxonomically similar to the natives, which may have facilitated their colonization in the island.

Puerto Rico’s subset of alien vines has a heterogeneous origin as reflected by the unequal contribution of each realm - variation in node size - to this pool of species (Fig. 2.3). The largest number of non-proliferating species has been contributed by the Neotropics (33 species) followed by the Indo-Malay and Afrotropics with 8 species each (Fig. 2.3a). The Neotropics has also contributed the largest number of proliferating species (12 species) followed by the Indo-

18 Malay (10 species) and Afrotropic (7 species) realms (Fig. 2.3b). Likewise, variation in the number of links between the two networks reflects the extent of vine exchange among the realms. For example, non-proliferating vines have arrived from more realms than proliferating species. Also, proliferating vines with an original Indo-Malay and Neotropical distribution are now widely distributed. Finally variation in link size indicates differences in the number of species moving between realms (Fig. 2.3). Among the Neotropical species found in Puerto Rico, more species with a non-proliferating than a proliferating status has reached the Neartic.

Likewise, the Indo-Malay realm has contributed the largest number of proliferating species reported in Puerto Rico (Fig. 2.3b).

The number of alien vines has increased through time but in a step-wise fashion (Fig. 2.4). It is possible to recognize three periods of introductions, the second being the shortest and one in which the largest number of alien vines were introduced. Distinguishing between proliferating and non-proliferating species revealed differences between them. Specifically, proliferating vines 1) are increasing at a slower rate than those with a non-proliferating status (Fig. 2.4), and

2) have been significantly longer in the island than those with a non-proliferating status (82.5 versus 66.6 years; t test, t = 18.7 df = 69 P < 0.001). Species introduced during the first and third period (before and after 1916) tended to have different number of uses, the latter having mostly a single use that included species with an ornamental and horticultural value (Table 2.2).

Origin, proliferation status, and traits

Vine origin was associated to one of seven traits in the subset of proliferating species (Table 2.3).

Specifically, alien proliferating vines included more species with horticultural/medicinal uses and fewer with medicinal uses than expected by chance whereas the opposite was true for their

19 native counterpart (Fisher’s exact test, P < 0.001). This led us to group alien and native proliferating species into a single group to examine the extent to which proliferating status was associated with the various traits considered in this study.

Family and proliferation status were not independent, with the Fabaceae contributing more proliferating and fewer non-proliferating species than expected by chance (Fisher’s exact test P <

0.001; Fig. 2.2a). The classification of vines according to distribution showed that the endemic and native species represented the smallest and largest fractions, respectively, of vines.

Distribution and proliferation status were not independent (χ2 = 47.52, df = 3, P <0.001; Fig.

2.2b), with alien naturalized vines including more proliferating species, and alien casuals and endemic including fewer proliferating species than expected by chance. Twenty five percent of all vine species are common, whereas 55% and 18% uncommon and rare, respectively (Fig.

2.2c). Local abundance and proliferation status were not independent (χ2 = 27.0, df = 2, P

<0.001; Fig. 2.2c). As expected, there are more proliferating species among the common species, and fewer among the rare species than expected by chance.

The vast majority of vines reported in Puerto Rico are perennial with life span being independent from proliferation status (Fisher’s exact test P = 0.84, Fig. 2.2d). The most common climbing mechanisms among Puerto Rican vines include twining and the use of tendrils, and I found that climbing mechanism was independent from proliferation status

(Fisher’s exact test P = 0.080, Fig. 2.2e). Puerto Rican vines produce four general fruit types with most species having rhexocarpic and simple fruits (Fig. 2.2f). General fruit type and proliferation status were not independent (Fisher’s exact test, P < 0.001; Fig. 2.2f), with rhexocarpic fruits found more often and simple fruits less often than expected by chance among proliferating species. Legumes followed by septicidal capsules, pepos, and achenes were the

20 most common specific fruit types and as shown previously, fruit type and proliferation status were not independent (Fisher’s exact test, P < 0.001; Fig. 2.2g). The number of vines with legumes was greater than expected by chance among proliferating species whereas fewer than expected among the non-proliferating (Fig. 2.2g). Vines were characterized by one, two or more dispersal modes. Overall zoochory was the prevalent mode of dispersal followed by anemochory

(Fig. 2.2h). Dispersal mode and proliferation status were not independent (Fisher’s exact test, P

< 0.01; Fig. 2.2h), anemochorous species being less abundant among proliferating species than expected by chance alone.

Seventy nine percent of vine species had at least one reported use, whereas the remaining fraction none (Fig. 2.2i). Most species with one reported use were listed as having a medicinal or ornamental use, whereas those with two uses as having both medicinal and horticultural uses.

Use was not independent from proliferation status, with fewer species with no reported use found more often than expected among proliferating, than among non-proliferating species (Fisher test

P = 0.04; Fig. 2.2i).

Predicting vine proliferation status

Proliferation status in the island and invasive status elsewhere were not independent (χ2 = 28.81, df = 1, P < 0.001, Fig. 2.2j). Proliferating vine species in Puerto Rico are more often than expected by chance listed as invasive elsewhere in the World (Fig. 2.2j). Thus, invasive status elsewhere may provide a good indication of proliferation potential in the island.

The classification tree model showed a good agreement between the predicted and current proliferation status of vines (Kappa = 0.66) and a high predictive power (PCC = 85.4 %) both in its capacity to correctly classify non-proliferating (Specificity = 88.8 %) and proliferating

21 (Sensitivity = 77.5 %) species in Puerto Rico (Table 2.4). The tree model identified five variables, namely specific fruit type, use, abundance, distribution, and dispersal mode, as good predictors of proliferation status and split the species into eight terminal nodes or groups characterized by a unique combination of these traits (Fig. 2.5). Specific fruit type generated the first split in the data: the left branch (A1) included species producing 13 out of 25 specific fruit types whereas the right one (A2) the remaining types. The left branch of the tree was split into five groups based on use, distribution, dispersal mode, and specific fruit type. Four (G1, G3, G7, and G8) and one (G6: traits A1, B2, D2, F2) of these groups were predicted to contain non- proliferating and proliferating species, respectively (Fig. 2.5). Similarly, the right branch of the tree was split into three groups based on abundance and distribution. Two of these (G2: traits

A2, C2 and G3: traits A2, C1, E2) and a third one (G4) were predicted to contain proliferating and non-proliferating vines, respectively (Fig. 2.5). The random forest analysis produced a mean tree model with a slightly lower predictive power than the classification tree to correctly classify vine species proliferating status (PCC = 74.0%). The mean model had a good capacity to correctly classify vine species as non-proliferating (specificity = 81.3%) but compared to the tree classification model had a reduced capacity to correctly classify vine species as proliferating

(sensitivity = 56.0%; Table 2.4). This translated into an overall reduced model accuracy (Kappa

= 0.37). However, the random forest selected the same variables as the classification tree to predict proliferating vines, namely fruit type, use, dispersal mode, distribution, and local abundance (Table 2.4). These are the variables that make the biggest contribution to either the accuracy of the final tree model or the homogeneity of the groups (Gini index) in which the vine species were classified (Fig. 2.6).

22 DISCUSSION

I assembled a database that included information on the distribution, intrinsic, and extrinsic traits of vines to characterize an insular assemblage, examine the relationship between vine proliferation status and these traits, and predict vine proliferation status. All but two traits were associated with vine proliferation status. Once all the variables were examined simultaneously in my classification tree models, I was able to successfully predict vine proliferation status based on five of these, namely fruit type, use, abundance, distribution, and dispersal mode. Species incorrectly classified as proliferating or non-proliferating provided further insights into factors underlying vine proliferation status. Overall, my approach can be extended to other functional groups of plants in an effort to limit the introduction of potential invaders, monitor alien and native species likely to proliferate, and manage landscapes invaded by alien species or encroached by native ones.

Composition of an insular vine assemblage

A comparison with other tropical insular and mainland sites puts Puerto Rico second after

Singapore in terms of climbing species richness (area of Puerto Rico and Singapore is 9,104 km2 and 704 km2, respectively; Croat 1978, Whistler 1992, Bush et al. 1995, Muthuramkumar and

Parthasarathy 2000, Chong et al. 2009). In terms of family composition, my results are consistent with work conducted in the New World showing that 1) among families with a major component of climbing species the Fabaceae followed by the Asteraceae have the largest number of species and 2) among families with mostly climbing species the Asclepiadaceae followed by the Convolvulaceae, Cucurbitaceae, Dioscoreaceae, and Smilacaceae include the largest number

23 of vines whereas the Passifloraceae contains the largest number of lianas (Gentry 1991). Puerto

Rico’s contemporary vine assemblage includes an increasing number of alien species - 28% of all vines are casual or naturalized aliens, as well as numerous proliferating alien and native species - 30% of all vines are proliferating. The Fabaceae and the Convolvulaceae families contributed the largest number of proliferating species with 32 and 13 species, respectively, but their origin varied. Next in importance were the Araceae, Cucurbitaceae, Asteraceae, and

Vitaceae families contributing between 3 and 4 proliferating species each. Two non-mutually exclusive hypotheses may explain these patterns. First, in Puerto Rico the occurrence of native proliferating vines partially reflects the overall richness of these families. Second, the occurrence of alien proliferating vines may reflect the uses given to these plants (see below).

Time since collection among the alien species provides further evidence that vine use has been important in invasion processes. The steep increase in the number of recorded introductions after 1916 coincides both with 1) the beginning of the “Scientific Survey” of

Puerto Rico and the Virgin Islands, which included extensive plant surveys in the island (Liogier

1996), and 2) the establishment of the Agricultural Experimental Station which introduced a large number of alien plant species to the island (e.g., Telford and Childers 1947). The latter partially explains the large number of vine species with horticultural uses introduced during that period (Table 2.2).

Origin, proliferation status, and traits

Three traits, namely fruit type, dispersal mode, and use, were associated with proliferation status.

Fruit type and dispersal mode are traits that reflect various qualities of propagule pressure such as seed number and dispersal distance (Martínez-Ghersa and Ghersa 2006, Ibáñez et al. 2014). I

24 found that more proliferating species than expected by chance produced rhexocarpic fruits, i.e., dehiscent fruits derived from one flower and two or more carpels with seeds shed through sutures and openings of the pericarp, whereas more non-proliferating species than expected produced simple fruits. Not surprisingly the largest number of species with rhexocarpic fruits were in the Fabaceae and Convolvulaceae. Likewise, the largest number of species with simple fruits not only were in the Asteraceae, Cucurbitaceae, and Passifloraceae, but also included most endemic species. The genus Mikania in the Asteraceae has the largest number of endemic species in the Caribbean islands (Francisco-Ortega et al. 2008). Thus, in the context of my work fruit type seemed to play an important role in the proliferation of vines.

Plant use on the other hand, is an extrinsic trait that reflects socio-economic conditions, land use patterns, and cultural values (Thuiller et al. 2006, Dehnen-Schmutz et al. 2007), but also sowing intensity or propagule pressure (Pyšek et al. 2009) may favor the introduction and spread of species. In my study, proliferating vines were concentrated in a small number of families and tended to have a reported use compared with their non-proliferating counterpart. In Puerto Rico, proliferating alien species (e.g., Fabaceae) have been introduced with the purpose of establishing cover crops aimed at increasing crop yields and controlling erosion (Kinman 1916, Telford and

Childers 1947, Más and Lugo-Torres 2013). Families with proliferating species (e.g., Fabaceae,

Convolvulaceae, Araceae, Cucurbitaceae, and Dioscoreaceae) are also widely recognized as economically important due to their horticultural (edible fruits, tubers, cover crops), ornamental, and medicinal value (Gentry 1991, Langer and Hill 1991, Daehler 1998).

Predicting vine proliferation status

25 Examining individual traits as wells as the relationship between global invasive and local proliferation status was useful to better understand the role of these variables in "predisposing" vines to proliferate (Maillet and Lopez-Garcia 2000, Herron et al. 2007). Yet, it was the classification tree and the random forest models that allowed the identification of vine groups characterized by a unique combination of traits and the identification of proliferating vine based on these traits.

The model produced false positives (species predicted as proliferating in Puerto Rico when they are not), as well as false negatives (species predicted as non-proliferating when in fact they are listed as proliferating in Puerto Rico) that deserve further scrutiny. The false positives included 15 species belonging to 11 genera and 7 families. Eight of these species are considered invasive outside of Puerto Rico (e.g., Thunbergia alata Bojer ex Sims and Ipomea indica

(Burm.) Merr; Sherley 2000, Gallagher et al. 2010). Two non-mutually exclusive hypotheses may explain these false positives. First, these species may reach a proliferating status in the near future but currently have not had enough time to either escape from cultivation to establish wild populations or adapt to the environmental conditions of the new habitat (Harris et al. 2007, Pyšek et al. 2009). This time lag is evident in Puerto Rico where on average the proliferating aliens have been more time than non-proliferating aliens (82 vs. 67 years). Second, extrinsic traits not considered here such as the properties of the invaded landscape may have limited the ability of these alien species to proliferate (Lonsdale 1999, Foxcroft et al. 2011). The false negatives, on the other hand, included 31 species in 23 genera and 15 families. Nineteen of these species are listed as invasive in several regions of the World (e.g., Cryptostegia madagascariensis Bojer ex

Decne, Mucuna pruriens (L.) DC, Ipomoea violacea L., and Passiflora foetida L.; Kairo et al.

2003, Space and Imada 2004, da Silva et al. 2008, US Forest Service 2013). Two non-mutually

26 exclusive hypotheses may explain these false negatives. First, other traits not considered here such as those reflecting the physiological and demographic requirements of the plants may help differentiate species within a given family or genus that was incorrectly classified (Lonsdale

1999, Thuiller et al. 2006, Gallagher and Leishman 2012, Ibáñez et al. 2014). Second, habitat characteristics describing specific edaphic or disturbance conditions may improve the ability of the model to correctly classify the species proliferation status (Kueffer and Daehler 2009). The drawback of including more variables to this approach is an increasingly complex model.

Vine invasion in a changing World

At this point it is important to highlight extrinsic traits (Horvitz et al. 1998, Lonsdale 1999,

Thuiller et al. 2006) or system context and habitat susceptibility characteristics (Foxcroft et al.

2011) not considered here that may play a critical role in the proliferation of vines in years to come. Likewise, it is possible that the same factors underlying the proliferation of lianas in forested habitats (Dillenburg et al. 1993, Phillips et al. 2002, Schnitzer and Bongers 2011) may explain the proliferation of vines in fragmented habitats and abandoned agricultural lands.

At least four hypotheses have been proposed to explain increased liana and vine abundance in forests and human modified ecosystems. First, changing management regimes of pastures and sugar cane fields through fire suppression and Green Cane Trash Blanketing-GCTB is mentioned as an important factor contributing to vine spread in these environments (Dias-Filho 1994, Chaila et al. 2005, Seeruttun et al. 2005). Second, natural (Horvitz et al. 1998, Laurance et al. 2001,

Kirkham 2005, Schnitzer and Bongers 2011) and anthropogenic (e.g., Mackey et al. 1996,

Blaustein 2001, Kirkham 2005) disturbances acting alone or synergistically contribute to an increase in liana and vine abundance in a variety of ecosystems. In agroecosystems, frequent

27 sowing of vine seeds sharing similar traits combined with altered soil, irradiance, and/or biotic conditions including grazing pressure may facilitate vine proliferation at local to regional scales

(Mackey et al. 1996, Lake and Leishman 2004). Likewise, the abandonment of farms associated with boom and bust socio-economic cycles may contribute to vine proliferations at large-scales

(Forseth and Innis 2004, Kirkham 2005). In rural areas of Central and Western Puerto Rico, vines cover extensive areas devoted formerly to agriculture (Delgado and Restrepo in prep.). In many of these areas vine seeds were sowed to establish cover crops and/or improve forage

(Kinman 1916, Telford and Childers 1947). In these same areas farmers have linked the proliferation of vines in recent times with hurricane activity (C. Restrepo, unpublished data). In contrast, in the largest tract of old growth forest of the island long-term studies have shown that vine abundance has not increased after hurricane disturbance (Chinea 1999, Rice et al. 2004,

Royo et al. 2010).

The other two hypotheses put forward to explain the proliferation of lianas and vines are related to increases in atmospheric CO2 and climate change. Increasing levels of CO2 can directly (Sasek and Strain 1988, Granados and Korner 2002, Wang et al. 2010) and indirectly

(Wang et al. 2010) influence patters of carbon allocation and growth. Given that concentrations of CO2 can vary locally and regionally (Gurney et al. 2012) it would be interesting to evaluate the response of vines in habitats with increased concentration of CO2 as it has been done for trees in urban areas (e.g., Searle et al. 2012). Decreasing rainfall coupled with increasing temperatures and seasonality are known to underlie an increase of liana abundance in forests

(Wright et al. 2004). A recent study with one vine species supports this hypothesis. Ipomea cairica (L.) Sweet increased shoot biomass, decreased root biomass, and produced leachates with strong allelopathic effects under increased temperatures (Wang et al. 2011). Under a scenario of

28 climate change and large-scale human populations shifts from rural to urban settings I expect vines to proliferate in fragmented forest habitats and abandoned agricultural lands. Therefore, a focus on post-agricultural areas merits further consideration.

Conclusion

Puerto Rico is one of several regions around the world where vines are proliferating in a variety of altered habitats. I successfully used plant traits to characterize the proliferating and non- proliferating components of this diverse regional vine assemblage, and predict their proliferation status. A classification tree model identified vine groups characterized by different suites of traits and successfully predicted vine proliferation status based on these. The false positives and false negatives provided valuable information about species with potential for proliferation or additional traits to consider. Overall, my approach can be extended to other functional groups of plants in an effort to limit the introduction of potential invaders, monitor alien and native species likely to proliferate, and manage landscapes invaded by alien species or encroached by native ones.

CHAPTER TWO

TABLES

30 Table 2.1 Vine traits included in the databases with the corresponding sources of information.

Trait type Attributes Categories Source of trait definition Source of trait data

Taxonomy Family Family names Axelrod 2011 Acevedo-Rodriguez 2005; Axelrod and 2011; TROPICOS database

Distribution Genus Genus names Axelrod 2011 Acevedo-Rodriguez 2005; Axelrod 2011; TROPICOS database

Species Species names Axelrod 2011 Acevedo-Rodriguez 2005; Axelrod 2011; TROPICOS database;

Origin Native, Alien _ Acevedo-Rodriguez 2005; Axelrod 2011

Distribution* Endemic, Native, Casual alien, Richardson et al. 2000 Acevedo-Rodriguez 2005 Naturalized

Origin by realm* Afrotropics, Australasia, Indo-malay, Olson et al. 2001 Acevedo-Rodriguez 2005; Axelrod Neotropics, Oceania, Paleartic 2011

Current distribution Afrotropics, Australasia, Indo-malay, Olson et al. 2001 TROPICOS database Neartic, Neotropics, Oceania, Paleartic

Time of earliest Year _ UPRRP and MAPR Herbaria collection

Local abundance* Common, Uncommon, Rare see Methods Axelrod 2011

Proliferation Proliferating in Puerto Yes (includes native encroaching and see Methods E. Santiago (pers. obs.) and D. status Rico alien invasive), No Delgado (pers. obs.) and Más, Lugo-Torres 2012

Invasive outside of Yes, No see Methods see Appendix 1 Puerto Rico

Intrinsic Life form Vine, Liana Gentry 1991 Acevedo-Rodriguez 2005; see

Life span* Annual, Perennial, Annual/Perennial Raven et al. 2005 Plants USDA database

31 Climbing mechanism* Active: Aerial Roots, Tendrils, Acevedo-Rodriguez 2005 Acevedo-Rodriguez 2005 Twining; Passive: Sarmentous, Scandent, Spines Fruit type (general) Simple, Compound, Rhexocarpic, Spjut 1994 van der Pijl 1972; Acevedo- Schizocarpic Rodriguez 2005; Appendix 1

Fruit type (specific)* Fleshy: Acrosarcum, Amphisarcum, Spjut 1994 van der Pijl 1972; Acevedo- Bacca, Baccarium, Bibacca, Drupe, Rodriguez 2005; Appendix 1 Pepo, Syconium; Dry: Achenarium, Achene, Caryopsis, Ceratium, Craspedium, Cypsela, Denticidial capsule, Disclesium, Fissuricidal capsule, Legume, Loculicidal capsule, Lomentum, Polachenarium, Pyxidium, Samara, Samarium, Septicidial capsule, Utricle

Dispersal mode* Autochory, Anemochory, Hydrochory, van der Pijl 1972 see Appendix 1 Zoochory

Extrincic Use* Handicraft, Horticultural, Ornamental, see Methods See Appendix 1 Medicinal, No use reported

Appendix 1is available upon request.

Table 2.2 Uses given to introduced (casual alien and naturalized) vine species according to time since first time of collection in Puerto Rico.

Time Single use Multiple No use period uses Medicinal Horticultural Ornamental

1884-1915 5 4 3 20 0

1916-2008 4 9 9 13 3

Table 2.3 Results from Fisher's exact test of independence between native and alien proliferating species. Type of trait Trait P-value

Intrinsic Local abundance 0.152 Life span 1.000 Climbing mechanism 0.267 Fruit type (general) 0.459 Fruit type (specific) 0.342 Dispersal mode 0.350 Extrinsic Use < 0.001

34 Table 2.4. Accuracy measures for the Classification Tree and Random Forest models to predict the proliferation potential of vine species based on vine species attributes.

Classification Tree Random Forest Accuracy Metric Model Model

Kappa 0.66 0.37 PCC 85.40% 73.80% Specificity 88.80% 81.30% Sensitivity 77.50% 56.20% Error Rate 14.60% (Null error) 30.00% OBB estimate error rate 26.20%

Kappa or Cohen’s kappa is a measure of the agreement between the predictions and the actual values, corrected for agreement resulting from chance alone. PCC is the percentage of correctly classified vine species. Specificity and Sensitivity represent the percentage of non-proliferating vine species and proliferating vine species, respectively, correctly classified. The OBB error or the "Out of the Bag" error rate is the mean estimate of the classification error.

CHAPTER TWO

FIGURE LEGENDS

36 Figure 2.1 Climbing species classified according to life form, origin, and proliferation status.

Life form and proliferation status were not independent (G2 = 10.0, df = 2, P = 0.007) with lianas less likely to be among the proliferating species than expected by chance.

Figure 2.2. Vine species classified according to proliferation status and a) Taxonomic affiliation,

(b) Distribution, (c) Abundance, (d) Life span, (e) Climbing mechanism, (f) General fruit type,

(g) Specific fruit type, h) Dispersal mode, (i) Use, and (j) Invasive status elsewhere in the world.

For each attribute I show the resulting Chi-square or Fisher’s exact test p-value. The asterisks indicate the cells that made the largest contribution to the results. Family: Dio (Dioscoreaceae),

Orc (Orchidaceae), Poa (Poaceae), Sap (Sapindaceae), Pas (Passifloraceae), Ast (Asteraceae),

Apo (Apocynaceae), Cuc (Cucurbitaceae), Cov (Convolvulaceae), Fab (Fabaceae), Other (32 remaining families), Specific fruit type: FisC (Fissuricidal Capsule, Bam (Baccarium), Dru

(Drupe), LocC (Loculicidal capsule), Bac (Bacca), Fol (Follicarium), Ach (Achene), Pep (Pepo),

SepC (Septicidal capsule), Leg (Legume), and Other (16 remaining fruit types). Dispersal mode:

Ane (Anemochory), Auto (Autochory), Hy (Hydrochory)], Zo (Zoochory). Use: Hand

(Handicraft), Hort (Horticultural), Med (Medicinal), and Orn (Ornamental).

Figure 2.3. Networks showing the exchange of alien (a) non-proliferating (n = 49) and (b) proliferating (n = 27) vine species reported for Puerto Rico based on their original and current realms of distribution. The nodes (circles) and edges (arrows) represent the biogeographic realms and exchange among them, respectively. Node size indicates the number of vine species that come originally from each realm and edge size the number of species moving between realms, with edge color matching the realm of origin of the species.

Figure 2.4. Cumulative distribution of the earliest year of collection of each of the 76 vine species introduced to the island of Puerto Rico. Three periods can be recognized: 1) 1884-1912

(8 species or 0.3 species/year), 2) 1913-1915 (24 species or 12.0 species/year), and 3) 1916-2008

(38 species or 0.4 species/year.

Figure 2.5. Classification tree showing the proliferation status of Puerto Rican vines based on the attributes identified with an asterisk in Table 1. The letters identify the variables chosen by the model to split the species during successive model iterations: (A1) Species producing achene, acrosarcum, amphisarcum, ceratium, disclesium, drupe, follicarium, pepo, polachenarium, pyxidium, samara, syconium, and utricle fruits; (A2) species producing achenarium, bacca, baccarium, caryopsis, craspedium, denticidial capsule, fissuricidal capsule, legume, loculicidal capsule, lomentum, samarium, septicidal capsule fruits; (B1) species with ornamental/medicinal, horticultural/medicinal, horticultural, handicraft uses, and no known use; (B2) Species with horticultural/medicinal/ornamental, horticultural/ornamental, medicinal and ornamental uses;

(C1) rare and uncommon species; (C2) common species; (D1) endemic and casual alien; (D2) native and naturalized alien species; (E1) endemic, native and casual alien; (E2) naturalized; (F1) anemochory, autochory and zoochory dispersal syndrome; (F2) anemochory/zoochory/hydrochory, zoochory/hydrochory and hydrochory dispersal syndrome;

(G1) species producing achene, acrosarcum, ceratium, polachenarium, samara and utricle fruits;

(G2) species producing drupe, follicarium and pepo fruits.

Figure 2.6 Importance of vine traits as predictors of proliferation status in the random forest analyses. Importance is given by (a) the mean decrease in accuracy and (b) the mean decrease of the within group homogeneity (Gini index) caused by the random removal of traits and species.

39 FIGURE 2.1

1 Non−Proliferating Proliferating

2 1

0 1

m 0

0 2

Vines Lianas Vines Lianas

Native Alien

40 FIGURE 2.2

0 0 0

4 0 0

1 2 2 (a) Family (b) Distribution (c) Abundace (d) Life span Non−Proliferating

0 2 Proliferating

0 0

5 5

1 1

0 0 c p < 0.001 p < 0.001 p < 0.001 p = 0.803

4 8

0 0

* 1 1

0 0

3 6

i V *

0 0

2 4

0 0

5 5

0 0 * * * 1 2 * * 0 0 * 0 * 0 s c t r c n n e l l rc ra a io s s c v b e i ve n d r a a r A o D a A u o a h m ti lie ze o o a u i e O P P A C C F t e a a li m m R n n /P O d l a n n n n N a r m m e n u u o o A r E s t C c e A a a n P C N U

0 0 0

0 2 0

1 1 1 (e) Climbing mechanism (f) General fruit type (g) Specific fruit type (h) Dispersal mode

0 0

8 8

0 0

8 4

0 0

6 6 c p p p p

e = 0.280 < 0.001 < 0.001 < 0.001

0 0

s 6 3 *

0 0 n *

4 4

0 0 *

4 2

0 0

2 2 0 * 0 2 1 *

0 0 0 0 s s s l o r r o r t t s il g le d ic ic C u c C h p C g s e to r o e e r e o u n e r in p n p p m r a o c c e p e r n u d o h h d th o to e in d n u r r a n D B F A P e L e A A y Z t t y n d p n i im o a a B e o th H O O H /O l R e n e w p c c D L S / / / o a S T T S o o O e o o o ia m c m x iz n o t r r S o e h A Z u /Z e a C h c A e A S R n S A

2 (i) Use (j) Invasive elsewhere

s p = 0.030 0 p < 0.001

s 4 *

2 * 0 *

5 * *

0 0 t n d d d d n d d n e o s r r e n n e r e e r s e o O a a O O u N Y H M H M t− M M − o H t− r − − d N t− r o d rn e r o H n M o H a O − H H rt o H

41 FIGURE 2.3

(a) Non-Proliferating Paleartic

Neartic Indo-Malay

Afrotropics

Neotropics Australasia

Oceania

(b) Proliferating Paleartic

Neartic Indo-Malay

Afrotropics

Neotropics Australasia

Oceania

42 FIGURE 2.4

7 All alien Non-Proliferating alien Proliferating alien

0 I II III

1900 1920 1940 1960 1980 2000

Earliest Year Recorded

43 FIGURE 2.5

44 FIGURE 2.6

(a)

Distribution Fruit Type Fruit type Use Dispersal mode Dispersal mode Local abundance Distribution Climbing mechanism Local abundance Use Climbing mechanism Realm of Origin Realm of Origin Life span Life span

0 20 40 60 80 5 10 15 20

Mean deacrease in accuracy Mean deacrease in Gini index

(b)

0 20 40 60 80 5 10 15 20

Mean deacrease in accuracy Mean deacrease in Gini index

CHAPTER THREE

ABIOTIC AND BIOTIC VARIABLES IN THE ASSEMBLY OF VINE COMMUNITIES

ALONG A COMPLEX ENVIRONMENTAL GRADIENT

47 INTRODUCTION

The assembly of plant communities is a dynamic process controlled by abiotic and biotic factors that can limit or facilitate the establishment of species at multiple scales (Keddy 1992, Weiher et al. 1998, Cornwell and Ackerly 2009). Abiotic factors determine the fundamental niche of a species (Toledo et al. 2012, Pottier et al. 2013) and influence the strength and type of biotic interactions that unfold in a community (Callaway and Walker 1997, Callaway et al. 2002).

Biotic factors, on the other hand, further restrict a species niche, delimiting their realized niche

(Meier et al. 2010). For the most part, community studies emphasize facilitative or competitive interactions (Callaway and Walker 1997) while host - parasite interactions are less studied even though they also play an important role in community assembly (Dobson 1990).

In host-parasite systems two sets of interactions, those involving host and parasites, and those involving different parasites, may occur simultaneously and influence each other (Dobson 1990).

The type, size, and quantity of hosts available in an area will influence the type and abundance of parasites that can become part of a community. Likewise interactions between parasites such as facilitation can reduce host specificity, thus influencing the effect that host availability might have on community assembly. The strength of host-parasite interactions can be further influenced by environmental factors through their direct effect on host availability and diversity.

For example, productive environments often provide a great variety of hosts, thus facilitation between parasites may provide little advantage to them (Thrall et al. 2007). Under harsh environmental conditions, the opposite is true. Parasites with little host specificity and those that establish positive interactions with other parasites most likely will be successful in colonizing and spreading (Thrall et al. 2007). Thus, examining host – parasite interactions in tandem with

48 abiotic factors can increase our understanding about the mechanisms underlying the assembly of communities.

Climbing plants and their hosts represent a particular example of host - parasite interactions.

Often referred to as structural parasites, climbing plants depend on other plants or surfaces for structural support (Stevens 1987, Putz and Mooney 1991, Laurance et al. 2001). Thus, host availability and diversity most likely influences the structure of climbing plant communities

(Nabe-Nielsen 2001). So far, most climbing plant community studies have focused either on the influence of abiotic variables (Molina-Freaner et al. 2004, DeWalt et al. 2006, Swaine and Grace

2007, DeWalt et al. 2010) or climbing plants hosts’ preference associated with the diversity of climbing mechanisms that they exhibit (DeWalt et al. 2000, Nabe-Nielsen 2001, Carrasco-Urra and Gianoli 2009, Leicht-Young et al. 2010). This is particularly true for lianas - a group of woody climbing plants – on which most studies focus (but see Molina-Freaner et al. 2004).

These studies have shown that liana diversity increases with increasing annual precipitation

(Molina-Freaner et al. 2004), soil fertility (DeWalt et al. 2006), and precipitation seasonality

(DeWalt et al. 2010). Likewise it has been shown that tree diameter and particular climbing mechanisms are associated in ways that suggest host preferences among vines. For example stem twinners and tendril climbers prefer young trees with small diameters, whereas root climbers prefer old trees with large diameters (Nabe-Nielsen 2001, Carrasco-Urra and Gianoli

2009). This preference may limit stem twinners and tendril climbers to disturbed habitats and abandoned agro-ecosystems, whereas root climbers do not experience that limitation and can grow in forested areas. Host size, however, is not always a limitation for climbing plants;

Facilitation between climbing species provides an alternate structural host to climbing species that have the ability to use other climbers for support and as a way to reach a canopy of large

49 trees (Pinard and Putz 1992, Campanello et al. 2007). Only a small number of studies have examined both host preference and availability along with abiotic variables to explain climbing plant abundance and community composition (DeWalt et al. 2000, Ibarra-Manríquez and

Martínez-Ramos 2002, Leicht-Young et al. 2010), and none, to my knowledge, has paid attention to vines - herbaceous climbing plants - which are also increasing in abundance in agricultural and post-agricultural landscapes in different parts of the World (Mackey et al. 1996, Blaustein

2001, Kirkham 2005)

As vines grow and expand they can form patches (Starr et al. 2003)t hat can smother regenerating forests, crops and infrastructure alike over vast areas (Demers et al. 2012). Most of our knowledge about vines and their response to abiotic variables comes from studies on species of vines that have agricultural value or are considered invasive or weedy (Ludlow 1980,

Warshauer et al. 1983, Grof et al. 1990, Mackey et al. 1996). This bias limits our understanding about what is happening at the vine patch level in terms of composition and vine species interactions. That approach ignores the relative contribution and interaction between abiotic and biotic factors in the assembly of these patches. Likewise the strength of the influence of these interactions may vary with scale (Leibold et al. 2004), which highlights the need to examine them at multiple scales in order to describe the possible patterns that may arise. Taking a community approach to understand the increase in vines abundance we can gain a better understanding of the trajectories followed by vine patches as they assemble, as well as the influence biophysical variables have on them (Booth and Swanton 2002). A community approach can also have practical applications, because it allows us to model and predict how vine patches will respond under disturbances such as climate change, invasion by new species and/or human management (Booth and Swanton 2002).

50 With this motivation I decided to use land cover variables as a surrogate for structural hosts in order to understand how the interaction between abiotic factors and structural host preference may influence the assembly of vine communities. Here, I used a multi-scale approach to examine if the relative contribution of abiotic and biotic factors on the assembly of vine communities changed across scales. Specifically I asked 1) How does vine patch diversity and composition vary along a mosaic of varying environmental and land cover conditions? 2) What is the relative contribution of these variables to vine diversity and composition within these patches? 3) Is there evidence of facilitation or positive association between species inside vine meta-communities? I hypothesized that abiotic factors (i.e. environmental variables) will have a stronger influence on vine composition than biotic factors. In particular, I believe that precipitation and seasonality variables will have the most influence on both vine diversity and composition, with diversity increasing with increasing precipitation and decreasing seasonality, given vines’ stems vulnerability to cavitation under severe droughts (Gartner et al. 1990). However, I also expect that biotic factors will play a weaker, but important role in both determining vine diversity and the dominant climbing mechanism. Specifically, that twining vines and tendril climbers will dominate areas with young forest or areas with host with small diameters, while root climbers will dominate forest areas with large and old trees. Nevertheless, this pattern will only hold under conditions of limited facilitation between vine species.

METHODS

Study area

My study area in central Puerto Rico encompasses 1,763 km2 or equivalently 20 % of the island

(Figure 3.1). The area running from the northern to the southern coast of the island encompasses

51 diverse bioclimatic, geologic, edaphic, and topographic condition. The northern karstic belt and the central volcanic range are humid, whereas the southern karstic belt is dry (Mitchell 1954,

Daly et al. 2003). The different bioclimatic conditions that occur in the area in combination with the varying geology have resulted in a wide variety of soil types (http://www.nrcs.usda.gov).

This mosaic of biophysical conditions supported a thriving agricultural economy that included coffee, tobacco, and sugar cane plantations in the late 1800s and early 1900s (Mintz 1953,

Sánchez Korrol 1983). Yet, starting in the 1950’s the island shifted to an industrial economy, causing the abandonment of plantations, and large-scale recovery of vegetation (Grau et al.

2003). Today, secondary forests, active coffee plantations, and pastures are the dominating land covers in this area. Also I have estimated that vine patches currently cover ~ 3% or 49.5 km2 of the whole region and locally up to 4% or 12 km2 in municipalities like Utuado, that used to be the center of coffee production of the island of Puerto Rico (Delgado et al. in prep.).

Structure and composition of vine patches

I used a map depicting all vine patches with areas ≥ 75 m2 to select 51patches distributed throughout the study area to visit them and characterize their vine structure and composition

(Figure 3.1). For the creation of this map I used high-resolution aerial photographs (3001 Inc.) taken between November 2006 and March 2007 to extract vine patches (See Chapter 4.). The centroid coordinates of each vine patch were uploaded into a GPS (Trimble Geo XM 2005 series) that was used to navigate and locate the patches in the field. Discrepancies between the mapped and current vine patch location due to growth or manual elimination were noted and new coordinates were taken.

52 The patches visited in the field were classified according to size into two groups: small (5 - 10 m radius) and large (>10 m radius). At each patch I used a variation of the point-intercept method to characterize species composition and abundance (Caratti 2006). A varying number of random points (12 and 20 points for small and large patches, respectively) ≥ 1 m apart were selected to sample vines. The number of selected points was based on a pilot study at 10 vine patches of varying size that showed that this number was sufficient to include most if not all the vine species present. At each random point, a sampling pole (2.5 cm in diameter) running vertically through the top layer of the vine patch was introduced and all live vine stems and/or leaves that touched the terminal 10 cm of the sampling pole were identified to species. My sampling strategy allowed me to account for the irregular shapes of vine patches, the arrangement of leaves stacked in multiple layers, and the 3-D structure of vine patches when growing on other vines and existing structures of different heights.

The above data was used to calculate the relative abundance (A; Equation 1) and the mean relative density (D; Equation 2) of each vine species within a patch.

∑푛 푃 퐴 = 𝑖=1 𝑖 (퐸푞. 1) 푛푝

Relative vine abundance was expressed as the number points within a patch in which a vine species touched the pole (P) divided by the total number of points (np) sampled in a vine patch.

퐶 ∑푛 푃 ( 𝑖 ) 𝑖=1 𝑖 ∑푛 퐶 퐷 = 𝑖=1 𝑖 (퐸푞. 2) 푛푝

Vine relative density (D), on the other hand, was expressed as the number of times green stems or leaves of a vine species touched the pole (C) per point (P) divided by the total number of leaves and stems that touched the sampling pole at each point. I then calculated the mean relative density of each vine species per vine patch. All vine species present in a patch were collected, identified to species, and voucher specimens were deposited in the herbarium of the Universidad de Puerto Rico- Rio Piedras (UPRRP). Plant nomenclature follows Axelrod (2011).

Environmental and land cover data

Vine patch neighborhoods were characterized at three different scales based on environmental and land cover data (Table 3.1). The environmental data allowed me to describe the fundamental niche of the vine species, whereas the land-cover data - a surrogate for the host characteristics at the scale of this study - the realized niche. The land cover data used in this study describes the age of the forest and the heterogeneousness of the land cover, both in terms of diversity of covers and level of disturbance. All of which can make it a good surrogate for describing the structural composition of the forests (see below). The environmental geodatabase included three climatic, seven edaphic, and two topographic variables that were successively transformed to derive new variables (Table 3.1). All resulting raster layers were rescaled to 90 m and cut to the same extent of the Digital Elevation Model (DEM; seamless.usgs.gov) of the island.

To generate 19 bioclimatic variables I used monthly maximum and minimum temperature, and total monthly precipitation maps (Daly et al. 2003) with the Bioclim algorithm of dismo package in R version 3.1.2. I examined the degree of correlation among the 19 bioclimatic

54 variables and selected those least correlated among them (Pearson correlation values < 0.65).

Bioclimatic variables meeting these criteria included those that represented extreme (Bio 5, Bio

6, Bio 18, Bio19) and annual variability (Bio 2, Bio15) conditions in both temperature and precipitation, respectively. The raster layers representing this subset of bioclimatic variables were then used in a Principal Component Analysis (PCA) to reduce both the redundancy and dimensionality of the data (Table 3.2). The first two axes derived from the PCA were chosen because together they explained 80.2% of the variation in the bioclimatic data (Table 3.3). Two variables (Bio 18 and Bio 19) were positively correlated with the first component (Climate 1), whereas two other (Bio 5 and Bio 6) were negatively correlated. On the other hand, only one variable (Bio 15) was positively correlated with the second component (Climate 2), whereas two variables (Bio 2 and Bio 19) were negatively correlated.

I used the soil map of Puerto Rico from the USDA Web Soil Survey datasets

(http://websoilsurvey.sc.egov.usda.gov) and the soil characterization database of the National

Cooperative Soil Survey (http://ncsslabdatamart.sc.egov.usda.gov) to generate maps of seven edaphic variables. Soil types were grouped at the taxonomic level of great group and for each one we selected soil chemical (i.e., pH, cation exchange capacity - CEC, percent of organic, and inorganic carbon) and physical (i.e., available water content - AWC, clay content, and bulk density) properties of the top, soil layer (0-20 cm). The resulting vector layers were rasterized with a 90 m resolution and used in a PCA. The first two axes derived from the PCA were chosen because together they explained 68.2% of the variation of the soil characteristics data (Table

3.4). Three chemical soil properties (mean pH, CEC and percent of inorganic carbon) were positively correlated with the first component (Soil 1). On the other hand, two soil physical properties (Clay content and AWC) were positively correlated, whereas one physical property

55 (Bulk density) was negatively correlated with the second component (Soil 2). Both Principal

Component Analyses were preformed using R version 3.1.2.

The two topographic variables were generated using the Digital Elevation Model (DEM) for

Puerto Rico (90 m resolution). Both slope (in degrees) and aspect were derived from the DEM using ArcGIS 10.0.

To generate meaningful land-cover variables from the point of view of vines I used the land cover map generated by Helmer et al. (2008). Three variables derived from this map could indirectly account for vine “host” characteristics, namely vegetation structure and neighborhood characteristics. This land cover map, in addition to mapping the different land covers present in the island of Puerto Rico also includes information of forest age. In this map, forests were classified according to their geology, life zone, and age. In order to reduce the dimensionality as well as the redundancy of the data, we reclassified the forest pixels to solely reflect their age.

My reclassified map included seven land cover classes (Table 3.1). These new land cover units were renamed to reflect the level of disturbance they experienced with high urban density receiving a value of 1 and forest age 4 a value of 7. This reclassified map was used to derive three new land cover variables, namely Majority, Variety and Range, which reflected three different neighborhood properties. Using the focal statistics function in ArcGIS 10.1 I generated three land cover variables representing the predominant land cover type (Majority; M), the number of different land cover types present (Variety; V) and the range of different disturbance levels (Range; R). To calculate R, I used the difference between the lowest and highest disturbance value in the neighborhood. R values range from 0 to 6, where a value of 0 denotes a completely homogenous neighborhood with only one type of disturbance level present, whereas

56 a value of 6 denotes a neighborhood with both, areas of high urban density and areas of old forest (forest age 4).

Multi-scale analyses

To examine the influence of scale of environmental and land cover variables on the characterization of vine patches I performed a moving window analysis in ArcGIS 10.1 using three neighborhood sizes on the 90-m maps: small (2 x 2; 180 m2), medium (3 x 3; 270 m2), and large (4 x 4; 360 m2). This analysis calculates the value of the central cell of a neighborhood of a specified size and shape according to the values of the surrounding cells that conform the neighborhood, without changing the resolution of the maps. In cases where the neighborhood is made up of an even number of pixels, the cell that is immediately bellow and to the left of the center of the moving window becomes the processing or central cell in the analysis. The moving window analysis can calculate a variety of statistic measures, in my case, for all environmental layers I calculated the mean value in each neighborhood. To characterize each vine patch, I extracted the values of each environmental and land cover variable previously generated, using the centroids of the vine patches sampled in the field. For this process I used package raster in R version 3.1.2. In the case of aspect, I extracted the continuous values and reclassified them into nine categories (F: flat, N: North, NE: Northeast, E: East, SE: Southeast, S: South, SW:

Southwest, W: West, and NW: Northwest).

Data analysis

For each scale I ran three types of analyses, stepwise multiple regressions to model vine diversity as a function of environmental and land cover variables, hierarchical cluster analyses to examine

57 the species composition of vine communities and vine species associations, and a non-metric multidimensional scaling ordination (NMDS) to describe the relationship between vine patch composition and environmental and land cover variables. The NMDS ordinations were run on a species abundance and species density matrix. Environmental and land cover matrices for each scale were used to examine the correlation of these variables with the ordination axes. A functional level composition matrix, which included information on the proportion of species of each climbing group and origin class, was also used to examine how they correlated with the ordination axes.

In order to examine the influence of environmental and land cover variables on vine species diversity I calculated diversity (Shannon –Weaver) and evenness (Pielou) measures for each vine patch. The first one takes into account both species richness and abundance whereas the latter focuses on how equally abundant are all species present in a site (Colwell 2009, Jost 2010). I ran stepwise multiple regressions to fit models that could best explain the calculated diversity measures based on my environmental and land cover variables (Table 3.2). This analysis selects the best regression model based on the Akaike information criterion (AIC) which measures the goodness of fit of the model and also accounts for model complexity (Quinn and Keough 2002).

For the cluster analysis, I used the site x species abundance matrix for all 51vine patches and calculated the dissimilarity (in species composition) between vine patches using the Jaccard’s dissimilarity index and the Ward’s minimum variance method as the clustering algorithm.

(McCune and Grace 2002). The Jaccard’s index calculates the distance between communities in terms of how different they are in terms of species composition, without taking into consideration absence data. This clustering method evaluates the distance between elements, in my case vine patches, and clusters them together in a way that minimized within cluster variance

58 (Murtagh and Legendre 2011). I ran a second cluster analysis to examine the association between vine species, also using the Jaccard’s dissimilarity index and Ward’s minimum variance method as the clustering algorithm. I also calculated the standardized chi-square distance between every pair of vine species using PC-ORD software version 5 (MjM software). All pair of species with a Standardized Chi-square distance value ≥ |0.50|, were considered as being associated.

I ran two additional NMDS analyses. The first was based on a site x vine species, in which vine abundance was expressed in terms of relative vine abundance and the second NMDS, where abundance was expressed in terms of species density (see above). In order to examine the importance of functional groups in the composition of vine patches I generated a third matrix based on site x functional group matrix in which vine species were classified according to climbing mechanism (i.e., twining, tendrils, aerial roots, scandent; Putz and Holbrook 1991) and origin (i.e., native or alien). The proportion of each climbing group in a patch was calculated as the total abundance of all the species with the same climbing mechanism present in that patch divided by the total abundance of species in that vine patch. Under the scandent group we also included all species with a sarmentous mechanism. Similarly, the proportion of each origin class was calculated as the abundance of each native or alien species present per vine patch, divided by the total abundance of all the species present in that vine patch. This new matrix was then used in the NMDS ordinations in order to examine the influence of each variable on vine patch composition.

In all NMDS ordinations, differences in community composition among vine patches were calculated using the Bray-Curtis dissimilarity index (McCune and Grace 2002). I used my environmental and land cover matrix to examine the correlation between these variables and the differences in community composition shown by the vine patches. Graphically, only variables

59 that resulted significantly correlated (α = 0.1) with the NMDS axes were drawn as vectors in the ordination plot.

All statistical analyses were run in R 3.1.2, except for the chi-square test to examine association between vine species. All diversity measures were calculated using the vegan package (Oksanen et al. 2013) in R 3.1.2, as well as the NMDS analyses and the cluster analysis.

RESULTS

A total of 49 vine species, representing 33 genera and 16 families, were represented in the vine patches (Table 3.5). The family with largest number of species was the Fabaceae (17 species) followed by the Convolvulaceae (8 species) and the Cucurbitacea (4 species). Sixty three percent of the species (31 species) were rare (present in 1-5 vine patches), 28% (14 species) were common (present in 6 – 19 vine patches) and 8% (4 species) were very common (present in ≥ 20 vine patches; Figure 3.2a). Twenty-nine species (59%) were native, while 20 species (41%) were alien; this proportion between native and alien species changed among the four species most frequently found in the vine patches sampled, with three native (Mikania micrantha Kunth and Ipomoea alba L. and Cissus verticillata (L.) Nicolson & C.E. Jarvis; Table 3.5) and one alien species (Pueraria phaseolides (Roxb.) Benth. ). Thirty five species (71%), were twining vines while 9 species (18%) were tendril climbers; scandent and root climbers made up the remaining 11% of species, with the former group represented by two and the latter by three species, respectively. A log-linear model revealed that vine species origin, climbing mechanism and relative abundance in the landscape (i.e. rare, common or very common) were independent

60 of each other (G2= 13.12, df = 18, P = 0.78), thus neither origin, nor climbing mechanism explain the relative abundance of vine species in the landscape.

Diversity of vine patches

The number of vine species per patch varied between 2 - 15 (6.8 ± 2.3; mean ±SD; Figure 3.2b).

Diversity varied between 0.37 - 2.23 (1.46 ± 0.41; mean ±SD); the lowest two values (< 0.5) corresponding to vine patches with 2 and 4 dominant species, respectively (Figure 3.3a-b).

Evenness varied between 0.3 - 0.96 (0.78 ± 0.13; mean ± SD); only four patches had evenness values < 0.6. These four patches varied in total number of species; yet all of them were dominated by only one vine species, which further confirms that most patches are similar in terms of species abundance (Figure 3.3b), even when they differ in terms of number of species present

The multiple regression models predicting the Shannon-Weaver index of diversity as calculated using the relative abundance matrix at the three scales were all significant, but varied in complexity, retaining between two to four variables. However, all models had low explanatory power (R2 = 0.12 - 0.21; Table 3.6). The variables selected in the models differed depending on the scale, but Climate 1 and Slope were retained in all models even though the former was only marginally significant at the large scale and the former was only marginally significant at the small and large scale. Both Climate 1 and Slope show a positive correlation with diversity

(Table 3.6). Two land cover variables were included in the models predicting diversity. V was selected in the small and medium scales and showed a negative correlation with diversity, whereas R was only selected in the medium scale and showed a positive correlation with diversity, albeit not statistically significant (α > 0.05). In terms of evenness all models were

61 significant and provided a higher explanatory power (R2 = 0.33- 0.39; Table 3.6) than those predicting diversity. These models varied in complexity, selecting between two and four variables. Climate1 and Slope were positively correlated with evenness in all models, while Soil

1 and V were negatively correlated with evenness at the small and medium scale, although Soil 1 correlation was not statistically significant (α > 0.05; Table 3.6).

Similar results were obtained for the multiple regression models predicting diversity as calculated using the relative density matrix. In terms of diversity, these models have a slightly higher explanatory power (R2 = 0.15- 0.27; Table 3.6) than those generated using the relative abundance data. Another difference is the inclusion of Climate 2 in the models for the medium and large scale showing a negative correlation with diversity, although it was only marginally significant at the medium scales. In terms of evenness these models have slightly lower explanatory power (R2 = 0.21- 0.36; Table 3.6) than those generated using the relative abundance data. Another differences are the inclusion of the variables Climate 2 and Soil 2, both showing a negative correlation with evenness, yet none is statistically significant.

In general, both diversity and evenness were positively correlated with Climate1 in all the models developed (P = 0.10 – 0.001). This composite, which is positively correlated with precipitation and negatively correlated with temperatures, shows that both climatic factors are significant predictors of both diversity and evenness. Among the land cover variables, V was consistently retained in al models for the small and medium scales, showing a negative correlation with both the Shannon-Weaver index and evenness. For models explaining the

Shannon-Weaver index and evenness, R2 showed the highest values for the medium scale model, the model that also shows to be the more complex in terms of the number of variables retained

(Table 3.6).

Composition of vine patches

The clustering analysis separated the 51 vine patches into eight groups (Figure 3.4). The first and most distant cluster (a in Figure 3.6) includes 21 vine patches. Vine patches in this cluster are characterized by the presence of Ipomoea alba and Pueraria phaseoloides and a high proportion of twining vines. The second (b in Figure 3.4) and third (c in Figure 3.4) clusters include 4 and 7 patches, respectively. Cluster b is characterized by the presence of Vigna luteola and Ipomoea tiliacea, while cluster c is characterized by the presence of Syngonium podophyllum. The remaining five clusters are characterized by different species; cluster d, e, f and g are all characterized by the dominant presence of only one species, Antigonon leptopus, Cissus verticillata, Mucuna pruriens and Mikania micrantha, respectively (Figure 3.4). On the other hand cluster h is characterized by the presence of two species, Gouania lupuloides and M. pruriens.

Associations between vine species

A second clustering analysis grouped species commonly found together in vine patches thus providing evidence of association between pairs of vine species (Figure 3.4). Rare species like

Merremia umbellata and Rhinchosa minima, which were only found in one vine patch together, appear closely together in the dendrogram, and appear as having a perfect positive association

(Standardized Chi-square distance =1.0). Other pairs of rare species that appear closely 6=0.70), and Vigna adenatha with Ipomoea batatas (Standardized Chi-square distance =0.57). The clustering analysis also grouped together rare and common vine species that appeared to be associated like J. fulminense and C. verticillata, where J. fulminense was only found in patches

63 with a high abundance of C. verticillata. Two of the most common vines species in my study area were also grouped together (I. alba and P. phaseoloides); they were found together in 71% of the vine patches in they were present, however they show a weak association (Standardized

Chi-square distance <0.50). The same happens with common species like M. charantia and

Dioscorea alata, which are found together > 50% of the vine patches where these species were found, yet they showed a weak association (Standardized Chi-square distance <0.50). These weak associations between common or very common species are in part due to the fact that these species are found in a large proportion of patches and they do not always occur with the same set of species.

Biophysical variables and vine patch composition

The NMDS ordinations using abundance and density data at three different scales showed similar but not identical results (Table 3.7, Figure 3.5-3.6). I focus here first on the vine abundance matrix and the small-scale results and later mention the differences found across scales and when using the relative density matrix. The results for the medium and large scale are found in Table 3.7 and Figure 3.8-3.9.

The NMDS ordination based on vine relative abundance yielded a three dimensional solution with a stress value of 0.15 (Figure 3.5). Vine patches along Axis 1 were separated by the abundance of A. leptopus, M. quinquefolia and I. tiliacea. A. leptopus, M. quinquefolia are negatively correlated, whereas I. tiliacea is positive correlated with Axis 1 (Figure 3.5a).

Likewise along the Axis 2, the distribution of vines patches depends on the abundance of

Ipomoea alba, P. phaseoloides and M. micrantha. The three species are positively correlated with Axis 2 and the closeness between the vectors for I. alba and P. phaseoloides shows an

64 association between these species (Figure 3.5a). On the other hand, the distribution of vine patches along Axis 3 depends on the abundance of C. verticillata, J. fulminense and M. pruriens.

C. verticillata and J. fulminense were negatively correlated, while M. pruriens was positively correlated with the Axis 3 (Figure 3.5b). Again, the closeness between the vectors for C. verticillata and J. fulminense shows an association between these species. Using the abundance of functional groups such as climbing group, vine patches were separated along Axis 1 by the abundance tendril climbers. Tendril climbers were negatively correlated with the Axis 1. Along

Axis 2 vine patch distribution depends of the abundance of twining species and root climbers

(Figure 3.5a). Unlike climbing mechanisms, origin – native versus alien vine species – was not correlated with any of the ordination axes.

Two climatic, one edaphic and one topographic variables explained variation among patches.

Climate 1 and Climate 2 were positively and negatively correlated, respectively with Axis 1, yet showed a moderate to low explanatory power (R2 = 0.29 and 0.20, respectively; Table 3.7). Soil

1 was significantly correlated with Axis 2, exhibiting a positive correlation, albeit with low explanatory power (R2 = 0.15; Table 3.7). Aspect was correlated with Axis 1 (R2 = 0.24; Table

3.7) showing that vine patches over flat areas are only similar in terms composition to patches in

NW slopes. Comparing the results of the ordinations across scales shows minor differences. For example at the medium and large scales Aspect was correlated with Axis 2, even though this correlation was only marginally significant (P =0.1). The same happens with Slope at the medium scale (Table 3.7).

The NMDS ordination based on vine relative density yielded a three dimensional solution with a stress value of 0.14 (Figure 3.6). In terms of species and climbing groups the results of this ordination did not differed from those of the ordination based on relative abundance data.

65 However in this case, origin was correlated with Axis 1, with alien vines showing a positive, and native vines a negative correlation with the axis (Figure 3.6a).

Of the three land cover variables representing the characteristics of the structural hosts available for the vines, only one, namely V, was found to be significantly correlated with the ordination axes. However this correlation only appeared to be significant at the medium scale when the ordination was preformed using the relative density matrix. V was negatively correlated with Axis 2, yet it is just marginally significant (Table 3.7, Figure 3.9a), which implies that host only has a small influence in the composition of vine communities.

Mapping the spatial distribution of the ordination scores shows that low values of Axis 1 are mostly restricted to the southern region of the study area (Figure 3.7). I expect this region to have a high abundance of A. leptopus, as well as find a high abundance of tendril climbers, yet a higher richness of vine species than patches in the central part of the study area, according to my results. Large values of Axis 1 are mostly found in the northern and central part of the study area, where I expect to find a high abundance of root climbers. On the other hand, the spatial distribution of Axis 2 scores does not show any discernible spatial pattern (Figure 3.7).

DISCUSSION

Examining vine communities along a complex environmental and land cover mosaic in the

Caribbean island of Puerto Rico, I found that both vine diversity and species composition within vine communities can be explained by a combination of composite climatic, edaphic, topographic and land cover variables. In particular, climatic variables such as those related to

66 extreme temperatures and precipitation (Climate 1) appeared to be the most important influencing vine diversity within vine patches.

As I hypothesized, vine community composition examined using both species abundance and density, were strongly influenced by environmental variables. This was particularly true for the composite variables Climate 1 and Climate 2, reinforcing the importance of climate as one of the main forces, driving the distribution of vine species across the studied landscape. Thus, I expect that any change in climate will have a strong effect in the species composition of these vine communities. Downscaled climate model for the Caribbean, show a general trend of temperature increase (Singh 1997) and decrease in precipitation (Neelin et al. 2006, Stephenson et al. 2014), which may favor a reduction in vine diversity and the establishment and prevalence of vines adapted to dryer climate. However, these changes in vine composition can also affect the other vegetation in the area, which serve as the vines’ structural hosts, and may be subjected to higher below ground competition for water and resources (Schnitzer et al. 2005, Swaine and Grace

2007).

Along with the climatic variables, one edaphic and both of the topographic variables were important in terms of vine species composition. Both types of variables contributed to the definition of the species fundamental niche, establishing the environmental limitations of the species. However in the case of species like M. micrantha, which was present in 78% of the vine patches sampled, these variables seem to exert little power limiting the distribution of the species. M. micrantha in particular has a large capacity to tolerate a wide range of environmental conditions and can even improve edaphic conditions in order to further promote the species growth and spread (Li et al. 2007), which helps explains the successful colonization and spread of M. micrantha across the study area. Slope and Aspect have been show to

67 influence the diversity and composition of plant communities in both temperate and tropical regions, thus showing their influence over microclimatic conditions, soil erosion and soil water content (Huebner et al. 1995, Nichols et al. 1998, Gallardo-Cruz et al. 2009). In consequence is logical to find they also influence vine species composition. However, in contrast to other studies (Huebner et al. 1995), my results show that slope is positively related to vine diversity.

This may be explained in part due to the use of several vine species for erosion control (e.g. P. phaseoloides) in areas with steep slopes (Teleford 1947).

In general, differences in scale had little influence on the variables correlated with vine patch composition, except in the case of land cover variables. The inclusion of land cover variables, particularly Variety (V), was important in terms of explaining both vine diversity and the composition of vine communities. In terms of vine diversity, the richness of land cover class in a neighborhood was negatively correlated vine diversity. This may be explained in part by the fact that only a small number of vine species can act as climbing generalist, climbing over a variety of different hosts, whereas other vines are limited by their climbing mechanism to a structural host of a specific diameter and form. Thus, areas with multiple land cover classes found closely together may be limiting the spread of specialist climbers. When examining composition in terms of species density, the richness of land cover classes became an important variable explaining the difference between vine patches. This shows that structural host availability does influence, although weakly, vine species composition. A large proportion of twining species appear to dominate vine patches in areas with high values of V or land cover class richness, root climbers dominate patches in more homogenous areas in terms of land cover. These areas with high values of V represent mostly forest edges adjacent to urban or pasture/agriculture areas. In those areas multiple types of structural hosts are available, either natural or human-made (e.g.

68 shrubs, fences, light poles), thus, is not surprising twining vines dominate these areas since they prefer small diameter hosts (Nabe-Nielsen 2001, Carrasco-Urra and Gianoli 2009). Root climbers, although not limited by the diameter or size of their host, prefer to climb large tree trunks (Nabe-Nielsen 2001), which are most commonly found in more homogenous areas, in terms of vegetation cover.

Overall, the results presented here agree with several studies, mostly done with lianas, where host preference among climbing plants with different climbing mechanisms has been shown

(DeWalt et al. 2000, Nabe-Nielsen 2001, Ibarra-Manríquez and Martínez-Ramos 2002,

Campanello et al. 2007). Nevertheless, the weak explanatory power of the only land cover variable included in my results can be due in part to the inherent limitations of satellite-generated data (Helmer et al. 2002), which often provides a great amount of noise. In this respect, the use of a multiple size neighborhood approach helped us uncover the influence of land cover variables on vine communities, an effect that may have been otherwise lost or overlooked. Also possible is that my three land cover variables were not enough to characterize the potential structural hosts of these vines and more information is needed to better understand the influence that the hosts have on vine spread.

Another possible explanation for the weak influence of land cover variables on vine composition may be the importance of facilitation between vine species in a community. The ability of a vine species to use other vine species for structural support may allow them to by- pass the limitations posed by of host preference and availability. Evidence of this behavior has been reported both for vines and lianas (Pinard and Putz 1992, Nabe-Nielsen 2001, Campanello et al. 2007) in both tropical and temperate regions. As part of my results, I found evidence of positive associations between seven pairs of vine species, of which more than half represented

69 pairs of species with different climbing mechanism. These findings coupled with field observations lead us to speculate that structural facilitation between climbing plant species (both woody and non-woody) may turn a host-specific climbing species in to a generalist, able to extend over different types of hosts and surfaces that otherwise would be inaccessible.

Consequently, increasing the amount of physical stress and competition for resources (Stevens

1987, Laurance et al. 2001) experienced by the host vegetation.

Conclusion

My study sought to understand the abiotic and biotic variables controlling the assembly of vine communities and my findings showed that both vine diversity and composition within vine communities can be explained by a combination of complex climatic, edaphic, topographic and land cover variables. My results also highlight the importance of understanding inter-species interactions within a community. This approach also provides practical applications, allowing me to model and predict how will future vine communities will look and how will they respond under disturbances such as climate change or species introductions.

CHAPTER THREE

TABLES

71 Table 3.1 Biophysical variables generated for my analyses.

Variable type Variable Original variables Data Map Source Analyses Analyses 2 Derived class type resolution variables (m)

Environmental Climatic Monthly maximum Raster 230 Daly et al. Derivation of 19 PCA PC1 - and minimum (2003) bioclimatic using the Climate 1 temperature (°C); Bioclim algorithm in R Total monthly PC2 - precipitation (mm) Climate 2

Edaphic Physical properties Vector - http://ncsslabda - PCA PC1 - Soil 1 -AWC, Bulk density tamart.sc.egov. and Clay content. usda.gov Chemical properties - CEC, Percentage of organic and PC2 - Soil 2 inorganic carbon content and pH

Topographic Digital Elevation Raster 90 www.seamless. Derivation of two - Aspect Model usgs.gov topographic variables using ArcGIS 10.1 Slope

Land cover Land cover Land cover and Raster 30 Helmer et al. Reclassification of land Neighborhood Majority (M) forest age 2008 cover classes into: Urban analysis of dominant density, Low Urban land cover class density, Pasture/Agriculture, Neighborhood Variety (V) Forest age 1 (14-23 yrs.), analysis of land Forest age 2 (24-36 yrs.), cover class richness Forest age 3 (37-53 yrs.), Forest age 4 (64-77 yrs.), Each new class was Neighborhood Range (R) assigned a value from 1-7 analysis range of according to their level of degree of disturbance (e.g. High disturbance density Urban = 1, while Forest age 4 = 7)

72 Table 3.2 Bioclimatic and edaphic variables and their mean values in my study area. Asterisk (*) shows the variables that were used in the Principal Component Analyses.

Variable ID Name Mean ± SD type Bio 1 Annual mean temperature (°C) 23.53 ± 1.39

Mean diurnal range (°C) (Mean of monthly (maximum - Bio 2* 11.46 ± 0.93 minimum temperature))

Bio 3 Isothermality ((Bio2/Bio7) * 100) 76.55 ± 2.18

Bio 4 Temperature seasonality (standard deviation * 100) 126.56 ± 8.36

Bio 5* Maximum temperature of the warmest month (°C) 30.65 ± 1.46

Bio 6* Minimum temperature of the coldest month (°C) 15.70 ± 1.38

Bio 7 Temperature annual range (°C) (Bio5-Bio6) 14.96 ± 0.95

Bio 8 Mean temperature of the wettest quarter (°C) 24.51 ± 1.19

Bio 9 Mean temperature of the driest quarter (°C) 21.99 ± 1.42 Climatic Bio 10 Mean temperature of the warmest quarter (°C) 24.92 ± 1.35

Bio 11 Mean temperature of the coldest quarter (°C) 21.94 ± 1.44

Bio 12 Annual precipitation (mm) 1773.22 ± 244.36

Bio 13 Precipitation of the wettest month (mm) 273.83 ± 33.72

Bio 14 Precipitation of the driest month (mm) 57.34 ± 12.24

Bio 15* Precipitation seasonality (coefficient of variation) 50.64 ± 6.59

Bio 16 Precipitation of the wettest quarter (mm) 710.89 ± 94.27

Bio 17 Precipitation of the driest quarter (mm) 196.84 ± 32.43

Bio 18* Precipitation of the warmest quarter (mm) 525.46 ± 139.97

Bio 19* Precipitation of the coldest quarter (mm) 205.00 ± 36.44

pH* Mean pH 6.10 ± 1.05

InC* Inorganic Carbon (%) 0.95 ± 1.84

OrgC* Organic Carbon (%) 1.98 ± 0.62

Edaphic CEC* Cation Exchange Capacity 27.27 ± 7.64

Clay* Clay content (%) 38.91 ± 7.50

Bulk* Bulk density 1.29 ± 0.10

AWC* Available water capacity 0.15 ± 0.03

Table 3.3 The results for the Principal Component Analysis of the selected Bioclimatic variables at the small scale (180m2) including the loadings for each variable. Equivalent results were found using the bioclimatic data for the different scale sizes.

PC1 PC2 PC3

Bio 2 -0.082 -0.438 -0.792

Bio 5 -0.520 -0.303 -0.187

Bio 6 -0.549 -0.074 0.291

Bio 15 -0.036 0.643 -0.410

Bio 18 0.549 -0.043 -0.178

Bio 19 0.345 -0.543 0.231

Standard deviation 1.692 1.397 0.981

Proportion of variance 0.477 0.325 0.160

Cumulative proportion 0.477 0.802 0.962

74 Table 3.4 The results for the Principal Component Analysis of the selected soil variables at the small scale (180m2) including the loadings for each variable. Equivalent results were found using the bioclimatic data of the different size neighborhoods.

PC1 PC2 PC3

Kw 0.530 -0.065 0.461

AWC -0.049 -0.508 0.182

Bulk density 0.213 0.583 0.053

Clay content -0.308 -0.449 0.257

CEC 0.483 -0.096 -0.390 pH 0.504 -0.394 -0.609

Organic C 0.298 -0.176 0.402

Standard deviation 1.549 1.455 0.957

Proportion of variance 0.343 0.302 0.131

Cumulative proportion 0.343 0.645 0.776

75 Table 3.5 Vine species found in the sampled vine patches. Relative abundance was calculated as the percentage of patches in which the species was found.

Family Id Species Relative Climbing Origin abundance mechanism (%) Acanthaceae Asgan Asystasia gangetica 1.96 Scandent Alien Acanthaceae Thala Thunbergia alata 27.45 Twining Alien Acanthaceae Thfra Thunbergia fragrans 15.69 Twining Alien Araceae Phcon Philodendron consanguineum 5.88 Aerial Roots Native Araceae Phhed Philodendron hederaceum 3.92 Aerial Roots Native Araceae Sypod Syngonium podophyllum 35.29 Aerial Roots Alien Asteraceae Birep Bidens reptans 1.96 Sarmentous Native Asteraceae Mifra Mikania fragilis 7.84 Twining Native Asteraceae Mimic Mikania micrantha 78.43 Twining Native Convolvulaceae Ipalb Ipomoea alba 49.02 Twining Native Convolvulaceae Ipbat Ipomoea batatas 5.88 Twining Alien Convolvulaceae Ipset Ipomoea setifera 13.72 Twining Native Convolvulaceae Iptil Ipomoea tiliacea 17.65 Twining Native Convolvulaceae Medis Merremia dissecta 1.96 Twining Native Convolvulaceae Mequi Merremia quinquefolia 21.57 Twining Native Convolvulaceae Meumb Merremia umbellate 1.96 Twining Native Convolvulaceae Tucor Turbina corymbosa 1.96 Twining Native Cucurbitaceae Caame Cayaponia americana 3.92 Tendrils Native Cucurbitaceae Mepen Melothria pendula 33.33 Tendrils Native Cucurbitaceae Mocha Momordica charantia 37.25 Tendrils Alien Cucurbitaceae Seedu Sechium edule 1.96 Tendrils Alien Dioscoreaceae Diala Dioscorea alata 23.53 Twining Alien Dioscoreaceae Dirot Dioscorea rotundata 7.84 Twining Alien Fabaceae Cacae Calopogonium caeruleum 1.96 Twining Alien Fabaceae Camuc Calopogonium mucunoides 3.92 Twining Alien Fabaceae Ceplum Centrosema plumieri 1.96 Twining Native Fabaceae Cepub Centrosema pubescens 9.80 Twining Native Fabaceae Lapur Lablab purpureus 5.88 Twining Alien Fabaceae Malat Macroptilium lathyroides 1.96 Twining Native

76 Fabaceae Mupru Mucuna pruriens 31.36 Twining Native Fabaceae Phlun Phaseolus lunatus 7.84 Twining Alien Fabaceae Phvul Phaseolus vulgaris 1.96 Twining Alien Fabaceae Pupha Pueraria phaseoloides 47.06 Twining Alien Fabaceae Rhmin Rhynchosia minima 1.96 Twining Alien Fabaceae Teunc Teramnus uncinatus 3.92 Twining Native Fabaceae Viade Vigna adenantha 1.96 Twining Native Fabaceae Vihos Vigna hosei 1.96 Twining Alien Fabaceae Vilong Vigna longifolia 1.96 Twining Alien Fabaceae Vilut Vigna luteola 21.57 Twining Native Fabaceae Vivex Vigna vexillata 7.84 Twining Native Malpighiaceae Hepur Heteropterys purpurea 5.88 Twining Native Menispermaceae Cipar Cissampelos pareira 7.84 Twining Native Oleaceae Jaflu Jasminum fluminense 5.88 Twining Alien Passifloraceae Parub Passiflora rubra 17.65 Tendrils Native Polygonaceae Anlep Antigonon leptopus 9.80 Tendrils Alien Rhamnaceae Golup Gouania lupuloides 13.72 Tendrils Native Sapindaceae SePol Serjania polyphylla 11.76 Tendrils Native Valerianaceae Vasca Valeriana scandens 5.88 Twining Native Vitaceae Civer Cissus verticillata 43.14 Tendrils Native

77 Table 3.6. Coefficients of the retained environmental and land cover variables in the stepwise regressions modeling diversity indexes. The diversity indexes were calculated using the relative species abundance matrix and the relative species density matrix.. Significance level (***) = 0.001, (**) = 0.01, (*) = 0.05, (†) = 0.1. Relative species abundance Relative species density Shannon-Weaver Evenness Shannon-Weaver Evenness

180m 270m 360m 180m 270m 360m 180m 270m 360m 180m 270m 360m

Intercept 1.59*** 1.69*** 1.28*** 0.83*** 0.86*** 0.07*** 1.59*** 1.69*** 1.14*** 0.83*** 0.84*** 0.69*** Climate1 0.08* 0.08* 0.08† 0.04** 0.04** 0.05*** 0.10* 0.10* 0.10* 0.04*** 0.04** 0.05** Climate2 ------0.09† -0.08 - -0.02

Soil1 - - - -0.02 -0.02 ------Soil2 ------0.02 -

Slope 0.02† 0.01 0.02† 0.01* 0.01* 0.01* 0.02* 0.02* 0.02† 0.01* 0.01† 0.01 Variety -0.13† -0.189** - -0.04* -0.04** - -0.18* -0.15** - -0.06** -0.04** - Range - 0.08 ------p-value 0.01 0.01 0.02 <0.001 <0.001 <0.001 0.001 0.001 0.01 <0.001 <0.001 0.001 R-squared 0.16 0.21 0.12 0.38 0.39 0.33 0.24 0.27 0.15 0.36 0.33 0.21

78 Table 3.7. Results fomr NMDS ordination. R-squared values of the correlation of environmental, land cover variables, climbing group and origin with the NMDS ordination axes determined using permutations tests (n= 1000). Significance levels: (***) = 0.001, (**) = 0.01, (*) = 0.05, (†) = 0.10 Predictor type Predictor NMDS1 NMDS2 NMDS3 Abundance Scale 180 270 360 180 270 360 180 270 360

Environmental Clm 1 0.294*** 0.286*** 0.296*** 0.124* 0.133* 0.126* - - - Clm 2 0.201** 0.208** 0.202** - - - 0.022 0.018 0.022 Soil 1 0.071 0.054 0.050 0.151* 0.191** 0.186* - - - Soil 2 0.047 0.023 0.047 0.020 - 0.004 - 0.002 - Slope 0.002 0.005 - 0.120* 0.114† 0.101† - - 0.011 Aspect 0.238* 0.185 0.144 0.167 0.211† 0.210† 0.194 0.238* 0.203 Land Use Majority 0.144 0.142 0.170 0.115 0.119 0.115 0.186† 0.191† 0.164 Variety - - 0.002 0.038 0.083 0.027 0.004 0.003 - Range - - 0.008 0.016 0.028 0.051 0.001 0.002 - Climbing group Twining 0.133* - - 0.534*** - - - - - Tendrils 0.571*** - - - - - 0.334*** - - Aerial 0.263*** - - 0.346*** - - - - - Roots Scandent 0.073 - - 0.017 - - - - - Origin Native 0.019 - - - - - 0.041 - - Alien 0.019 - - - - - 0.041 - - Mean Density Environmental Clm 1 0.305*** 0.302*** 0.307*** 0.167** 0.170* 0.170* - - - Clm 2 0.181** 0.189* 0.183* - - - 0.045 0.048 0.046 Soil 1 0.040 0.014 0.028 0.146* 0.162* 0.163* - - 0.027 Soil 2 0.031 0.022 0.030 0.001 0.011 0.048 - - - Slope 0.007 0.009 0.076 0.089† 0.077 0.077 - - 0.012 Aspect 0.230† 0.183 0.159 0.148 0.220† 0.159 0.190 0.239* 0.172 Land Use Majority 0.155 0.155 0.161 0.142 0.142 0.125 0.165 0.165 0.157 Variety - 0.008 0.025 0.051 0.095† 0.025 0.004 - 0.000 Range - 0.002 0.044 0.017 0.028 0.043 0.003 - 0.002 Climbing group Twining 0.037 - - 0.518*** - - - - - Tendrils 0.403*** - - 0.592*** - - - - -

79 Aerial 0.369*** - - - - - 0.229** - - Roots Scandent 0.082 - - 0.023 - - - - - Origin Native 0.394*** - - 0.154** - - - - - Alien 0.389*** - - 0.148* - - - - -

CHAPTER THREE

FIGURE LEGENDS

81 Figure 3.1 Map of the Caribbean region (a) including the island of Puerto Rico (b), also showing the study area (c) and the location of the 51 vine patches visited (shown by black dots). Variation on gray shades in the map shows the six different lifezones present in the island.

Figure 3.2 Rank abundance (a) and richness (b) of vine species per vine patch.

Figure 3.3 Distribution of (a) values of Shannon - Weaver diversity index, and (b) Shannon’s evenness index along the vine patches sampled.

Figure 3.4 Heatmap grouping the species by abundance and species association through out all vine patches. The intensity of red color in the cells of the heatmap represents the abundance of vine species in each vine patch. The rows in the heatmap represent the vine patches and are grouped according to the hierarchical cluster analysis preformed using the Jaccard's dissimilarity index. Each group shows total number of patches that makes up the group and each one is characterized by the high abundance of different vines species [(a) I. alba and P. phaseoloides;

(b) I. tiliacea and V. luteola; (c) S. podophyllum; (d) A. leptopus; (e) C. verticillata; (f) M. pruriens; (g) M. micrantha; (h) M. pruriens and G. lupuloides]. The columns of the heatmap show the different vine species, grouped according to a second hierarchical cluster analysis that shows association between species. The graph shows the distribution of the proportion of twining species present in each vine patch.

Figure 3.5 The non-metric multidimensional scaling ordination of sites and vine relative abundance at the small (180 m2) scale. Each gray circle represents a vine patch and the

82 proximity among circles represents how similar the patches are in terms of species composition.

(a and b) Vine species and climbing groups (α = 0.001 and 0.05, respectively) important in describing vine patch composition are shown as red and blue vectors, respectably (Te = Tendrils,

Tw = Twining, Sc = Scandent/Sanrmentous, AR= Aerial roots). (c and d) Environmental and land cover variables significantly correlated to the ordinations axes (α = 0.1) are shown as vectors. Green polygons group together patches according to the aspect classes that they belong

(F = Flats, NE = Northeast, E = East, SE = Southeast, S = South, SW = Southwest, W = West,

NW = Northwest).

Figure 3.6 The non-metric multidimensional scaling ordination of sites and vine relative density at the small (180 m2) scale. Each gray circle represents a vine patch and the proximity among circles represents how similar the patches are in terms of species composition. (a and b) Vine species (α = 0.001), climbing groups (α = 0.05) and origin (α = 0.05) important in describing vine patch composition are shown as red, blue, and purple vectors, respectably (Te = Tendrils,

(F = Flats, NE = Northeast, E = East, SE = Southeast, S = South, SW = Southwest, W = West,

NW = Northwest).

Figure 3.7 The spatial distribution of the scores of the first and second axis of the ordination.

The dots represent the location of the vine patches sampled throughout the sturdy area.

Figure 3.8. The non-metric multidimensional scaling ordination of sites and vine relative abundance at the medium (a-b), and large (c-d) scale. Each gray circle represents a vine patch and the proximity among circles represents how similar the patches are in terms of species composition. Environmental and land cover variables (α = 0.1) important in describing vine patch composition are shown as red vectors. Green polygons group together patches according to the aspect classes that they belong (F = Flats, NE = Northeast, E = East, SE = Southeast, S =

South, SW = Southwest, W = West, NW = Northwest).

Figure 3.9 The non-metric multidimensional scaling ordination of sites and vine density at the medium (a-b), and large (c-d) scale. Each gray circle represents a vine patch and the proximity among circles represents how similar the patches are in terms of species composition.

Environmental and land cover variables (α = 0.1) important in describing vine patch composition are shown as red vectors.

84 FIGURE 3.1

(a) (b)

(c)

85 FIGURE 3.2

0 4 (a)

0 s

N 1

0 10 20 30 40 50

Abundance Rank

c (b)

2 4 6 8 10 12 14 16

Vine species per patch

86 FIGURE 3.3

(a)

h .

0 10 20 30 40 50 Vine patches

. (b)

0 0 10 20 30 40 50 Vine patches

87 FIGURE 3.4

h g

f e d

l i l l

t t t t t

r r r r r r

c s s c c

e b a a p a u b a a n b n a p p u n b g e u e n n a d

i i t

o l l i

u l l u

a o a m u e

r r

u e a u e o

f l c o u l n

d u e e a u n o o u o

u h h e d

f f

d v p i 0.2 0.4 0.6 0.8 1

q a l a a l l s

b i u m m c

p p r p h

i s I

c a a l m c h c u

a p p i i g p o e p

e h i

i e o p h n l h p

p u a u V

e a h

a u

D a i

o I p h s a h e I y

I u e e e

a T

T e P V a

V A P L

G M P

H M e

A P S

P R C S

M C Proportion of twining vines

88 FIGURE 3.5

Stress = 0.15 (a) (b)

89 FIGURE 3.6

Stress = 0.14 (a) (b)

90 FIGURE 3. 7

NMDS1 NMDS2

91 FIGURE 3.8

(a) (b)

(e) (d)

92 FIGURE 3.9

(a) (b)

CHAPTER FOUR

INTEGRATING BIOTIC INTERACTIONS TO MODEL SPATIAL NETWORKS OF THE

SPREAD OF A STRUCTURAL PARASITE

INTRODUCTION

Invasive species are a major driver of biodiversity loss and ecosystem change worldwide (Lodge

1993, Vitousek et al. 1997, Chapin et al. 2000, McMichael and Buoma 2000, Galil 2007). The increase in global connectivity has facilitated the movement of invasive species to new habitats and increase the rate of invasion occurrences (Crowl et al. 2008). This only highlights the importance of understanding the underlying mechanisms as well as the factors influencing the success and further spread of the species (Crowl et al. 2008). Modeling the spread of invasive and infectious diseases (Crowl et al. 2008, Stricklanda et al. 2015), has increased our understanding of the factors that influence these processes (Hastings et al. 2005, Crowl et al.

2008). These efforts have also provided essential information for the control and management of invasive species (Vander Zanden and Olden 2008).

Several approaches that aim to understanding invasions or disease spread have focus on connectivity, as a way to determine the vulnerability of the system to the spread of invasive species or pathogen (Minor et al. 2009, Moore et al. 2015). Nevertheless these approaches not always take into consideration all the factors that contribute to these processes. This is particularly important because the success of invasive species, reflects a combination of interacting factors, which include the prevailing abiotic conditions (Lake and Leishman 2004), and the biotic interactions that take place in the invaded area (Mitchell et al. 2006). Of these factors, the influence of biotic interactions on invasive success is the least explored; yet it may be critical to gain a better understanding invasion processes (Guisan and Thuiller 2005, Hastings et al. 2005). In consequence, in order to gain a better understanding of invasion processes, and

95 increase the accuracy of our models we need to examine the influence of biotic interactions on invasions.

Biotic interactions can influence the abundance and overall success of invasive species in ways that may either favor the propagation and establishment of the species or constrain its spread (Belote and Weltzin 2006, Reinhart and Callaway 2006). Positive mutualistic interactions between native and invasive species may drive invasion success in harsh or stressful habitats, where this kind of interactions are more beneficial (Cavieres et al. 2008) and can increase invasive success (Fummanal et al. 2006, Reinhart and Callaway 2006). On the other hand negative interactions (e.g., competition) may constrain the abundance of the invasive species and reduce its rate of spread, even though they cannot prevent the invasion (Levine et al. 2004,

Hastings et al. 2005). Likewise, parasite colonization and establishment can be limited through host availability. The abundance and type of hosts available in an area can directly influence the type and abundance of parasites that can successfully invade that area (Dickie et al. 2010).

However, the magnitude of the host influence on parasites success, can be reduced by positive interactions between parasites thus increasing the species invasive success (Thrall et al. 2007).

Graph theory provides a way to model invasions and disease spread incorporating both abiotic and biotic factors in order to increase our understanding of these processes. Graph theory has been successfully used to model diverse biological systems (Proulx et al. 2005), yet often the generated networks are simple and spatially implicit networks (Otterstatter and Thomson 2007).

Greater complexity has been incorporated to networks used to represent landscape connectivity

(Urban and Keitt 2001, Minor and Urban 2008, Minor et al. 2009) due to the need to generate spatially explicit networks. In these networks each node has information about its geographic position. Spatially explicit networks have been used to successfully predict the spread of diseases

96 and invasive species across large geographical areas as well as to assess the resilience of these networks (Margosian et al. 2009, Lookingbill et al. 2010, Sutrave et al. 2012, Stewart-Koster et al. 2015). These networks, however, incorporate information of the habitats, both invaded and non-invaded, through which the species of interest can potentially move. Other approaches have added more complexity to these kind of networks by incorporating a temporal component to them that allows to examine changes on invasion processes through time (Ferrari et al. 2014),.

However, all of these types of networks ignore the biotic interactions that occur within each habitat. Thus, these types of networks only present the “highway” through which the pathogen or invasive species can move, without incorporating information on the interactions between the modeled species and both the other species present in that “highway” or interactions with the actual “highway”. Here interactions with the “highway” can translate to host-parasite interactions or even habitat/species interactions like the inclusion of demographic information of the model species in relation to the size of the habitats (Urban and Keitt 2001). Networks that do incorporate multiple interacting species or elements have proven to increase their predictions’ robustness (Brooks et al. 2008). However, these networks are rarely spatially explicit (Funk and

Jansen 2010), but can be highly complex, made up of coupled or interdependent networks

(Buldyrev et al. 2010, Funk and Jansen 2010) and difficult to analyze. A way to simplify this is by incorporating the information on both the abiotic needs of the spreading species and the multiple biotic interactions in which is involved in a single spatially explicit network.

Networks are often used to simplify the representation of complex systems of interacting elements, providing a way to characterize the structure, function, and dynamical properties of networks through the use of metrics. These metrics describe the networks, in terms of size and connectivity at local and regional scales. Networks also have emergent properties, such as its

97 structure or topology, which directly affects its vulnerability to changes caused by disturbances

(Minor and Urban 2008). A common topology found in social and biological networks is a Scale free structure (Barabási et al. 2001). Scale free networks are defined by a power-law distribution of its node degrees (Shen et al. 2015), which represents a networks with a small number of highly connected nodes (i.e., Hubs), whereas the majority of the nodes have a small number of connections. A type of network considered very vulnerable to the spread of diseases, with the hubs been quickly infected and further infecting the majority of nodes of the network (Jerger et al. 2007). Knowing the topology of a network allows us to predict how resilient is that networks to different types of disturbances. Furthermore, to examine this we can simulate different types of disturbances (e.g. hurricanes and management decisions) through the removal of nodes from the network (Urban and Keitt 2001) in a random or in a directed way. A resilient network will be able to maintain its structure and connectivity even after the loss of a high number of node or potential habitats. These characteristics make networks excellent tools to examine 1) the influence of interacting abiotic and biotic factors on the spread of an invasive species, 2) examine how resilient is an invaded landscape, 3) inform eradication and conservation plans of the most efficient way to control (Ferrari et al. 2014, Stewart-Koster et al. 2015), and predict and prevent further invasion (Sutrave et al. 2012, Stewart-Koster et al. 2015).

Invasive vines represent a unique system to investigate the importance of biotic interaction in invasion and disease processes. Vines are herbaceous climbing plants that require other plants for support. This had led them to be considered structural parasites (Stevens 1987, Putz and

Mooney 1991, Laurance et al. 2001). In many regions around the World vines grow profusely on plant canopies, ultimately forming dense patches that may persist and smother and kill the underlying vegetation (Forseth and Innis 2004, Zhang et al. 2004, Space et al. 2009, Demers et

98 al. 2012). The resulting vine patches are often composed of multiple species that include both native and alien species (Chapter 3). Therefore, biotic interactions occurring between vines and their hosts, as well as among vine species within a patch, may play an important role in vine invasiveness. For example, some vines are capable of climbing over other vine species and overcoming the limitations imposed by host availability (Pinard and Putz 1992, Campanello et al. 2007) developing a facilitative interaction with other vine species. On the other hand, vine species can develop competitive interactions for resources with other vines, which can result in the control or displacement of the less aggressive competitor (Shen et al. 2015).

Here I propose to use graph theory to accomplish four objectives. First, develop a spatially explicit approach that incorporates biotic interactions to host – parasite networks. This approach integrates the information of two overlapping and interacting networks to build a complex and weighted network that is spatially explicit and includes the distribution data of both the “hosts” and the “parasites” in the same network, thus facilitating its analysis. Second, I use Mikania micrantha in central Puerto Rico as a case study to implement the use of GIS data; field gathered data and rules derived from my study of vines to generate spatially explicit networks of the connectivity among structural hosts of the invasive vine M. micrantha. Thus, modeling the current and potential spread of the species throughout the landscape, taking into consideration the propagule load of invaded hosts. Third, I developed further the landscape approach and examined the influence of biotic interaction on the structure and connectivity of M. micrantha’s host networks through the creation of a theoretical biotic interaction scenario. Fourth, I examine the effect of different removal strategies on the structure and function of these networks. The principal goal of this chapter is to use graph theory to represent biotic interactions on the spread of an invasive vine at multiple scales. I believe that negative interactions between the modeled

99 vine species and other vines can limit their spread, increasing the disconnection in its network.

Consequently, we expect a significant increase in the number of disconnected components and a reduction in the redundancy of connections between habitats in networks where biotic interactions between vine species are taken into account.

INCORPORATING BIOTIC INTERACTIONS TO NETWORKS OF INVASIVE SPREAD-

GENERAL APPROACH TO CREATE COMPLEX WEIGHTED NETWORKS

I borrowed concepts from the epidemiology literature to design a network approach that modeled vine-host interactions and the spread of an invasive vine through a landscape in a spatially explicit weighted network. At the scale of my work I considered as a vine host/region in the landscape that share the same degree of susceptibility to vine patches, given their characteristics in terms of land cover, thus hosts are described in terms of mapped land cover units. This land cover map units characterize the different types of land cover classes as the host of an “infection” by vines (Figure 4.1a). Thus in my network model there can be two types of hosts, “uninfected” hosts or non-invaded habitats (i.e., areas without vine patches), and “infected” hosts (i.e., area with vine patches) all having different degrees of susceptibility to the infection.

I proceeded to create spatially explicit networks depicting the connectivity of host/regions to the invasion of an invasive vine. These networks take into account different types of biotic interactions, including the interactions with potential hosts, as well as interactions with other vine species. In these networks each node represents a host/region – an area with given land cover characteristics that confer it a determined susceptibility to become invaded by vines

(Figure 4.1b). These host/regions also differ in their environmental conditions and on the

100 documented presence of vine patches in them. The environmental condition characterizing the host/regions will determine if the host/regions are apt for the colonization and establishment of the “parasite” or the vine species being modeled, whereas the presence of vine patches describes the host/region as invaded or not (Figure 4.1c). All this information about the host/regions are contained within the host/regions as attributes and is used in an adjacency matrix that determines how the nodes in the networks are connected (Figure 4.1c).

In these networks, the edges that connect two nodes are weighted through a function that determines the probability of movement of vine propagules between potential hosts/regions depending on the type of nodes this edge is connecting. The weight values of edges connecting all possible pairs of nodes are contained within the adjacency matrix constructed for each network. This adjacency matrix is used along a series of rules to determine how the network is connected (Figure 4.1d). These rules include information about the immediate geographic adjacency between host/regions, as well as incorporate the influence of positive and negative interactions between the vine species being modeled and other vine species.

Mikania micrantha IN CENTRAL PUERTO RICO AS A CASE STUDY

My study encompasses a 20 x 53 km region in the island of Puerto Rico spanning from the northern (Arecibo) to southern (Ponce) coasts (Figure 3.1). This region includes diverse climatic and edaphic conditions, as well as historical land uses, that may partially explain the distribution and diversity of vine communities (Chapter 3). My ongoing studies show that vines are invading numerous habitats, presently covering 49.5 km2 or 3% of a test area and infecting 58% of the utility poles in the area. Among those vines, the most abundant in the areas is Mikania

101 micrantha Kunth. For this reason here I focus on M. micrantha a native, proliferating or invasive vine that is among the most abundant vine species in all Puerto Rico and considered an invasive species in several parts of the World (Soria et al. 2002, Zhang et al. 2004). I use M. micrantha as a model to understand the influence of biotic interactions (i.e., competition) among different vine species on the spread of this invasive vine. I decided to incorporate in my networks both the abiotic and biotic factors influencing the spread of this species (Figure 4.1c).

Implementation of the complex network approach

The above approach was implemented through the use of a combination of environmental and land cover layers of information in GIS, and field gathered data, to model the connectivity of host/regions to the invasion if of M. micrantha. The constructed networks incorporated both abiotic and biotic factors. The abiotic data was incorporated in the form of distribution maps of the M. micrantha within the study area, whereas the biotic information was incorporated in the form of a host susceptibility map, and a vine patches map (Figure 4.1c). I generated host susceptibility maps at two different resolutions, coarse and fine resolution, to take into consideration the influence of scale in the size and structure of my resulting networks. The final products of this approach are networks that effectively merge together two interconnected networks, the hosts and the vine patches distribution networks.

Host susceptibility map

I developed a susceptibility index (SI) to characterize land cover map units as potential host/regions in my study area. This index included a rating and weight value associated to the land cover variables relevant to the determination of said vine invasion vulnerability. This model

102 was loosely based on pollution vulnerability index models (Bennet et al. 1987, Babiker et al.

2005), which incorporates the magnitude of the influence of all possible variables that contribute to the vulnerability of a system. The vine patch susceptibility index I created is based on three land-cover variables (Equation 1)

푆퐼 = 푀𝑖 × 푊푀 + 푉𝑖 × 푊푉 + 푅𝑖 × 푊푅 퐸푞. 1

In this index, Mi, Vi, Ri represent the ranking values associated to each of the classes that conform these three land-cover variables (M, V and R) derived from a recent land cover map for

Puerto Rico by Helmer et al. (2008; See methods in Chapter 2), whereas W represents the weight associated to each land cover variable depending on their propensity to be invaded by vine patches (Table 4.4). These land cover variables characterize the structural “hosts” of vines and were generated using the focal statistics function in ArcGIS 10.1 (Esri, Redlands, California,

USA), inside of a neighborhood of 180 m2 (2x2 pixel window). These variables included the type of the predominant land cover class [M; Majority class], the number of different land cover classes present [V; Variety], and the magnitude of the difference between land cover classes in terms of disturbance [R; Range] inside a neighborhood. To calculate R, I used the difference between the lowest and highest land cover value in a neighborhood. R values ranged from 0 to

7, where a value of 0 denotes a completely homogenous neighborhood with only one type of land cover type; whereas a value of 7 denoted a neighborhood with both, areas of high urban density and areas of old forest (forest age 4). In order to calculate both the rating and weight of each variable I used field data from locations with and without vine patches. The rating of each class in each one of the three land cover variables was calculated based on the amount of vine patches present in each class (Table 4.1- 4.3). In consequence, land cover classes with a higher number of vine patches will end up with a high rating value, because they show a higher

103 susceptibility to the establishment of vines. The resulting ranking values were rescaled to have values between 0-100. On the other hand, to calculate the weights for each variable we fitted a binomial linear regression model assessing the influence of my three land cover variables to predict the presence of vine patches (Table 4.4). These models were fitted using field-generated data of the location of actual vine patches and areas without patches in the field. The coefficients of each land cover variable were used as the weights in my index with values ranging from -1 to

The resulting susceptibility index values were rescaled to values ranging from 0-50. This was followed by a classification of the susceptibility index values (Figure 4.1c), creating a two-level and a three-level susceptibility map. For the two level map, or the coarse resolution map, pixels with values between 0-25 were classified as having low susceptibility, and host/regions with values > 25 as having high susceptibility. In this map all adjacent pixels that belonged in the same susceptibility class were grouped together to form host/region. For the three level map, or the fine resolution map, pixels with values between 0-18 were considered of low susceptibility, values between 19-30 have medium susceptibility, while values > 30 correspond to areas with high susceptibility to vine patches. Similar to coarse resolution map, pixels were grouped together according to their susceptibility class and formed the host/regions. I decided to use both maps in order to examine the effect of scale on the size and structure of the resulting networks.

The coarse resolution map resulted in 8,471 host/regions with an average area of 0.20 ± 9.00 km2, whereas fine resolution map in 17,731 host/regions of an average area of 0.10 ± 3.22 km2.

104 Distribution map of M. micrantha

To model the potential distribution of M. micrantha within my study area I used two of the most commonly employed modeling approaches, Generalized Linear Models (GLM) and Generalized

Additive models (GAM; Aguirre-Gutiérrez et al. 2013), and used field-based vine abundance

(See methods of chapter 3) and environmental data as input for these models. Seventy percent of the species abundance data was used as training data (36 data points) to fit the distribution models using both modeling approaches, while the remaining 30% (15 data points) was used as test data to validate the predictions generated by the models.

I used bioclimatic, edaphic, and topographic variables to model the probability of occurrence of M. micrantha, in two complementary ways. First, I used single bioclimatic, edaphic, and topographic variables. Second, I generated indexes represented by PCA axes that explained 80% and 65% of the variability in the bioclimatic and edaphic variables, respectively. As part of the first approach I used six of the 19-bioclimatic variables and six of the eight edaphic variables generated in Chapter 3. The selected bioclimatic variables were those with low correlation values among themselves (Pearson correlation < 0.65), and they represented extreme values (Bio

5, Bio 6, Bio 18, Bio19) and annual variability (Bio 2, Bio15) in both temperature and precipitation, respectively. The selected edaphic variables represented soil chemical (i.e., pH, cation exchange capacity - CEC, and percentage of organic carbon) and physical (i.e., available water content - AWC, clay content, and bulk density) properties of the top, soil layer (0-20 cm).

As part of the second approach we ran PCA on the subset of six bioclimatic variables mentioned above, and selected the first and second axes of the PCA, which explained most of the variability in the data. Similarly, for the edaphic variables we selected the first two axes derived from the PCA done using the subset of edaphic variable mentioned above. In addition, both

105 approaches included the two previously generated topographic variables (i.e., Slope and Aspect).

All PCA’s were performed using R version 3.1.2.

I used two common approaches to model the distribution of M. micrantha, GLMs and GAMs.

GLMs provide a more flexible family of regression models that allows the use of either species abundance or presence/absence data because the distribution of the response variable can be non- normal, and/or binomial (Austin 2007, Quinn and Keough 2008). GAMs are an extension of the

GLM (Guisan et al. 2002). These statistical models try to maximize the quality of the prediction by estimating unspecific (non-parametric) functions of the predictor variables (Austin 2007). I decided to run the GLMs using the single environmental variables, whereas the PCA indexes of environmental variables were used in the GAMs in order to better fit the distribution of these composite variables. I used the Aikake information criterion (AIC) to choose which model best fitted the training data, and validated the prediction power of the model calculating the Receiver

Operating Characteristic (ROC) and its respective Area under the curve (AUC; Fawcett 2006,

Quinn and Keough 2008). A value of AUC = 0.5 indicates a random model and would appear in the ROC plot as a diagonal line. On the other hand, an AUC value of 1 would indicate a perfectly accurate model.

After running both modeling algorithms I found that the model generated using the GLM algorithm provided a more accurate predictive distribution model than the one using the GAM algorithm. For M. micrantha, the GLMs provided the best fit for the training data, as well as the most accurate prediction for the test data used (AUC = 0.911; Table 4.5). Therefore, I selected the distribution model generated using the GLM modeling approach and the resulting species probability map was a raster layer where each pixel had a value of the probability of M. micrantha occurrence ranging between 0-1 (Figure 4.1c).

106 The same modeling approach was also used to generate a species distribution map for the second most abundant and prolific vine species in my study area, Pueraria phaseoloides (Roxb.)

Benth. This distribution map was use to simulate competition between M. micrantha and P. phaseoloides based on the probabilities of occurrence of each species in each host/region. This data was used for the generation of M. micrantha’s host network under a scenario of competition.

Vine patch map

I re-scaled (0.3 to 1 m) and re-projected (State Plane Coordinate System for Puerto Rico and the

Virgin Islands using the North American Datum of 198) ortho-mosaics developed from natural color aerial photographs taken between November 2006 and March 2007 (3001 Inc.) to map vine patches using feature extraction software. During a pre-processing step, all large water bodies

(e.g. lakes or reservoirs) were eliminated from the images. Preprocessing of these data reduced the computational time required to complete all image analysis processes, as well as to improve the accuracy of this process.

The pre-processed images were used in IMAGINE Objective 2014 (Hexagon Geospatial) to identify and extract all areas of vine cover within the study area. IMAGINE Objective is an object-oriented feature extraction software that simulates human visual processing by taking into account spectral data as well as object-based measures such as shape, size, texture and shadow.

The inclusion of these measures in the extraction process allowed the software to differentiate vine patches from other types of vegetation. In order to train the program to recognize vine patches, I provided at least 25 training signatures of vine patches visible in the images per ortho- photo. Experience in the field facilitated the identification of areas covered by vines in the

107 images. I also provided the program with signatures of other elements in the images, such as other types of vegetation and human-made structures in order to teach the program what elements to exclude from the extraction. The result of the feature extraction process is a vector layer, one for each ortho-photo, where each feature represents a vine patch. These vector layers went through a post-feature extraction process, where we employed the smooth tool from the vector clean operators available in the IMAGINE Objective software. This tool smoothes the edges of all polygon objects in a vector layer in order to obtain a more representative shape of the vine patches.

In ArcGIS 10.1 (Esri, Redlands, California, USA) I merged all overlaying and contiguous features and eliminated all vine patches <80 m2 due to difficulty to visually corroborate their classification. Afterwards, I visually revised all images in order to exclude all areas mistakenly identified as vine patches from the map. Finally, all vector layers were merged to create a vine patch map for the entire study area (Figure 4.1c).

Extraction of input data for the complex networks

In ArcGIS 10.1 I added as attributes of each of the host/regions in the host/region susceptibility maps, information of M. micrantha and P. phaseoloides probability of occurrence. This information was averaged across each host/region from the species distribution map. I also merge together both the host/region susceptibility map with the vine patches map in order to calculate the number an area covered by vine patches in each host/region. To do this I used the

Union operation in ArcGIS 10.1. This operation results in a new vector layer with an attribute table in which each row identifies each of the elements, in my case vine patches, contained inside each host/region with a unique id. Then we can calculate the number and size of vine patches

108 found per host/region (Figure 4.1c). The same processes were followed with the coarse and fine resolution host/region maps, which resulted in coarse and fine resolution maps with 41% or

3,457 and 7,127 of their host/regions invaded by at least one vine patch, respectively. From this information we calculated a host virulence index (Equation 2) that characterizes the amount propagule load in each host region due to the number and area covered by vine patches.

(푃 × 퐴푝) 푉푖푟푢푙푒푛푐푒 = 퐸푞. 2 퐴ℎ

Here P represents the total number of patches found in a host/region, Ap is the total area covered by vines in a host/region, and Ah is the total area of the host/region. Virulence values ranged from 0 -53, the 0 representing areas without vine patches. I considered host as highly virulent when virulence ≥ 2, which characterizes hosts with ≥ 5 patches covering at least 0.02 km2.

Afterwards, a program in Python was implemented by R. Arce-Nazario to automatize the network construction process. The first step of the program was to classify all host/regions in terms of susceptibility, probability of M. micrantha occurrence and vine patch presence, and extract this node type information and assign it to the centroid of each host/region. Then the program assigns weights to each pair of node adjacent to each other. The edge weights represent the movement probability between two types of nodes and are based on the probability of propagule establishment in those nodes given their characteristics (see below). These weights vary for the different biotic interactions scenarios examined.

109 Building complex networks

I constructed twelve networks (three biotic interaction scenarios at two different resolutions, taking into consideration two levels of virulence) that represent the connectivity of host/regions for the invasion of M. micrantha. The first two biotic interactions scenario only take into consideration the host-parasite interactions, with the first one only including the host/regions invaded by the species and the second one including both invaded host/regions and those that can potentially become invaded. In contrast, the third scenario takes into consideration the effect of competition among different vine species, in addition to the host-parasite interactions. Virulence was included and quantified in these scenarios in two different ways, as the presence/absence of vine patches, or as a magnitude given by a vine virulence index (equation 2). In the presence/absence scenarios, a host was considered as virulent or invaded with the presence of at least one vine patch, indifferent of its size. In the high virulence scenarios, for a host to be considered virulent it most have a Virulence value ≥ 2.

The construction of these networks involved two major steps: 1) the integration of the host/region susceptibility data, the potential distribution of the modeled species, and vine patch presence data into a polygon layer, and 2) the generation of adjacency matrices, which include the rules for node connection in my networks (Tables 4.6-4.8).

I defined different node types based on the host/region attribute data (Table 4.6). For the coarse and fine networks we defined 8 and 12 types of nodes, respectively. These nodes vary in terms of host/region susceptibility to vine invasion, species probability of occurrence, and the presence of vine patches (Table 4.6). The edges of the networks represented the potential bidirectional movement of propagule between nodes. In order for the nodes to be connected by an edge they must be adjacent to one another. In my case adjacency is represented by habitats

110 sharing a border. All edges were weighted (Tables 4.7-4.8) by the probability of movement between each pair of nodes. The movement probability or edge weights were calculated based on the probability of propagule establishment in those nodes given their node type (Equation 3).

푃푃퐸 +푃푃퐸 푃 = 푖 푗 퐸푞. 3 푟푚 2

In equation 3, PPEi and PPEj represent the Propagule Probability of Establishment of any pair of nodes (Table 4.6) given the characteristics of the two nodes in question (Table 4.8).

However, for the scenario of the current spread of M. micrantha we only considered in equation

3 hosts with vine patches present such that a node was considered occupied by M. micrantha if it contained vine patches and had a high probability of occurrence of the species (≥ 0.50; Table

4.7).

Negative interaction networks

Here, I present a theoretical scenario of competition among vines. For this scenarios I established a new set of conditions of how would nodes connect among themselves (Equation 4).

In this equation C is a variable that can only take values of 0 or 1, representing the effect of competition between vines. C will be 0 when the destined host has vine patches present and the probability of occurrence of P. phaseoloides ≥ to the probability of occurrence of M. micrantha, under any other circumstances C=1.

푃푃퐸 +푃푃퐸 푃 = 푖 × (퐶) 퐸푞. 4 푟푚 2

111

Therefore, the competition network represents a scenario where P. phaseoloides is a better competitor than M. micrantha, thus the latter can invade nodes with vine patches present only when the probability of occurrence of P. phaseoloides < than the probability of occurrence of M. micrantha.

Networks of the high virulence scenarios

In order to differentiate between vine-invaded host/regions in terms of their propagule load, I created high virulence network scenarios. In these scenarios only host/regions with a virulence value ≥ 2 are the only ones considered as invaded. This rule was included in order to better simulate the real word, where the presence of one vine patch in an area does not necessarily mean that the vine species there are expanding and/or dispersing propagules. In terms of the network of the current spread of M. micrantha, the high virulence restriction rule allows me to create the network of the host/regions most important in terms of generating M. micrantha propagules and dispersing them. The network of potential spread of M. micrantha, on the other hand highlights the host/regions with the highest potential to be invaded by M. micrantha, because they are connected to virulent hosts. Finally, the competition network will show how another viene species can limit the potential spread of M. micrantha.

Network perturbation experiments

I performed a series of experiments whereby I removed nodes to examine the resilience of the different networks to perturbations. This node removal can simulate the effect that management or changes in land cover can have on the size and structure of the host/regions connectivity networks to the invasion of M. micrantha. Two different removal approaches were tested, a random removal and a targeted removal. Both removal approaches eliminated 10% of the nodes

112 in the network, however the random approach selected these nodes at random, while the targeted approach removed 10% of the node with the highest values of betweenness, which represents the most important nodes in terms of network centrality, thus providing shortcuts for rapid movement inside the network.

Network analysis

I calculated a set of local and global network metrics to examine and compare the structure and connectivity of each network. Local metrics included the degree and betweenness of each node.

Node degree - the number of nodes or neighbors that are connected to one node - is one of the most simple and useful metrics to calculate and provides a measure of how important each node is in terms of connectivity. Betweenness - the number of paths that cross through a node - is a measure of the importance of a node due to its location or centrality. Thus, nodes with high values of betweenness are those through which there is a high flow or movement across the network (Rayfield et al. 2011). In consequence these nodes are usually the first to be infected in a disease spread networks (Jerger et al. 2007). I also measured global metrics, or metrics that helped describe the network structure and connectivity between its nodes. Among these global metrics I calculated the number of components, the network diameter, the mean clustering coefficient and compared these results between networks. The number of component- the number of groups of nodes connected together but unconnected to any other nodes in the network- gives us an idea of how fragmented is the network (Brooks et al. 2008, Rayfield et al.

2011). The diameter- the longest path between any two nodes, where the path length between those nodes has the shortest possible length- gives us information of how fast the movement within the network (Bunn et al. 2000). The clustering coefficient- the average fraction of a

113 node’s neighbors that are also immediately connected with each other- (Brooks et al. 2008) is measured for each node in a network, yet I calculated the average of these measures in order to obtain the mean cluster coefficient for the network (Rayfield et al. 2011).

In order to determine if the structure of my networks resembles that of scale free networks, I fitted the node degree distribution of each of the constructed networks to a power law distribution and ran a power law goodness-of-fit distribution test, which employs a bootstrapping procedure. This goodness-of-fit test was done using the R package poweRlaw (Christley et al.

2005) and it tests the null hypothesis that the data used is generated from a power law distribution.

RESULTS

The coarse host-parasite network of current M. micrantha invasion has a total of 1,641 host/regions (nodes) with 2,228 edges, a mean node degree of 2.72 and a mean betweenness of

1,332. The nodes in this network were arranged into 86 components, the largest of which included the majority of the nodes (1,364; Table 4.9). Spatially this large core component connects the majority of the nodes in the northern part of the landscape, while the smallest components area distributed along the central and southern part of the landscape (Figure 4.2). On the other hand, the fine resolution equivalent of this network is made up by 3,014 host/regions and 5,853 edges. This network has a node degree of 3.88 and a mean betweenness of 6,430. The nodes on this network are arranged in only 10 components, one large one (2,988 nodes) and 9 nine small one composed of less than 10 nodes each (Figure 4.2 and Table 4.9)

The coarse and fine host –parasite interaction networks showing the potential spread of M. micrantha have 41% of their nodes invaded by vine patches of which 78% and 81/% contain M. micrantha, respectively. The coarse host-parasite potential network has a size of 2,251

114 host/regions with 3,312 edges, a mean node degree of 2.94 and a mean betweenness of 12,279.

The nodes in this network were arranged into four components, the largest of which included the majority of the nodes (2,240). This conformation results in small network diameter, and a relatively high mean clustering coefficient value (Table 4.9). This reflects how connected are the host/regions that make up the core component of the network. In contrast, the fine resolution host-parasite potential spread network has a size of 5,671 nodes with 12,246 edges, a mean degree of 4.32 and mean betweenness of 12,279. The size of this fine resolution network more than doubles that of the coarse potential spread network. However, with the increase in size also comes an increase in fragmentation, resulting in a network made up of 22 components, with a core component of 5,598 nodes. This core component even though larger in size appears to be less connected, thus resulting in a larger diameter, and a slightly smaller mean clustering coefficient than the values observed for the coarse resolution potential interactions network

(Figure 4.2, Table 4.9).

Size and connectivity of the negative interaction networks

When comparing competition networks in terms of their resolution we found differenced in size and connectivity. The fine resolution network is made up of a larger number of nodes and edges.

They more than double the observed size for coarse resolution competition network (Table 4.9,

Figure 4.2), yet shows a significant decrease in connectivity. This decrease in connectivity can be seen by a larger number of components, a larger diameter, and a lower value of mean clustering coefficient in the fine resolution network (Table 4.8). This can also be seen when examining the spatial distribution of both coarse and fine competition networks (Figure 4.4).

115 Here, several small isolated components appear in the central and southern part of the study area in the fine network, whereas the coarse network appears almost fully connected.

In general, the inclusion of a negative biotic interaction like competition reduced the size of the networks (Table 4.9). However, competition has little effect over the connectivity of the networks, as shown by the small changes in the values of node degree, diameter and clustering coefficient (Table 4.9). Spatially the inclusion of biotic interactions among vines appears to have little effect, due to the similarity of the potential spread networks and those representing the competition scenarios. Thus, competition appears to have little effect containing the spread of M. micrantha in the landscape (Figure 4.4).

Size and connectivity of the networks under the high virulence scenarios Focusing on the most virulent host/regions resulted in smaller and less connected networks. At the coarse scale the network of the current spread of M. micrantha has only 35 nodes and 46 edges arranged in only one component where most nodes have only one neighbor (Table 4.9).

The spatial distribution of these nodes is limited the northern and central region of the study area

(Figure 4.3). At the fine scale, the network of the current spread of M. micrantha, has 112 nodes and 119 edges, arranged in six components (Table 4.9). At this scale a small number of small components can be seen distributed in the southern region of the study area (Figure 4.4). The networks of the potential spread of M. micrantha both at coarse and fine scale resulted in larger and disconnected networks in comparison to those of the current spread of M. micrantha. At the coarse scale the network has a size of 489 nodes and 321 connections, arranges in 173 components, whereas at the fine scale the resulting network has 2,761 nodes and 2,662 edges arranged in 663 components. Both potential networks show a large number of small components

116 in the southern region of the study area, which represents vulnerable hosts that can in the future be invaded if long-distance dispersal events take place, either naturally or assisted by humans.

Again, competition had little effect in limiting the potential spread of M. micrantha at both scales. Nevertheless an interesting pattern arise because the inclusion of competition under these scenarios of high virulence resulted in the loss of nodes, but also in a decrease in the number of components and a slight increase in clustering coefficient. All of these suggest that the nodes that are been lost due to competition between M. micrantha and P.phaseoloides are peripheral in the network, thus have little impact in the connectivity of the network.

Network topology and node degree distribution

The node degree distribution for all networks, indifferent of biotic interaction, scale or virulence followed a similar pattern, where most nodes had small degree values and highly connected nodes were rare (Figure 4.5 - 4.6). However, after fitting a power–law distribution to the node degree data, for each network, and testing their fit, I found that only two networks under the presence/absence virulence scenarios and three networks under the high virulence scenarios showed a power-law distribution (Figure 4.5 - 4.6). The networks that showed this scale free structure were, under presence/absence virulence scenario, the fine scale networks representing the potential spread and competition scenarios (Figure 4.5e-f). Under the high virulence scenarios, the networks with a scale free structure were both the coarse and fine scale networks of competition, along with the coarse scale network of the current spread of M. micrantha. All other networks only approximated a power-law distribution of their node degree.

117 Perturbation experiments and network connectivity

The node removal experiments showed a reduction in the number of edges in all networks, yet the number was quite different depending on the removal approach used. Even though I removed the same amount of nodes during the random and directed approach, a directed removal had the biggest impact on network connectivity (Figure 4.7 and Table 4.10). The random elimination of nodes resulted in an edge reduction that ranged between 15 – 17% for the coarse networks and

16% for the fine resolution networks. On the other hand the directed removal of nodes resulted in an edge reduction of 88% for the coarse and 67% for the fine resolution networks (Figure 4.7).

In terms of number of components, all networks showed an increase in the number of components that formed the networks, where the biggest values correspond to networks that were subjected to a directed node elimination process. In terms of the size of the core component, the random node removal process generated core components that varied in size between 1,875 – 1,867 and 4,937 – 4,901 nodes for the coarse and fine networks, respectively.

However, the directed node removal process generated core components with 12 and 48 nodes at the coarse and fine resolution, respectively (Figure 4.7). These results coupled with the large decreased in mean node degree in networks subjected to directed node elimination show that the directed elimination process successfully disconnected all networks. After this process, all the characteristics of the networks drastically changed, with isolated nodes and small-size components dominating the networks.

118 DISCUSSION

Using different types of biotic interactions, scales and virulence level, we showed how the incorporation of biotic interactions reduced the size but not the overall connectivity of the networks. All networks approximated a power-law node degree distribution, yet only five out of the twelve constructed networks proved to have an actual power-law distribution, thus exhibiting a scale-free topology. Networks with this topology are very susceptible to directed attacks or disturbances. Thus, it was not a surprise that the node removal experiment showed that the best method to considerably reduce the connectivity of all networks of M. micrantha spread is a directed approach in which the most important nodes in terms of network centrality are eliminated (i.e., highest values of betweenness).

Scale and network connectivity

I found that the connectivity patterns observed across all scenarios of biotic interactions were maintained indifferent of the resolution data used to create the networks. At both the coarse and fine scale the competition between vine species had little influence over network connectivity.

However, the magnitude of influence that the different scenarios of biotic interactions had on network size and number of edges, varied depending on the resolution data used to create the networks. For example, the fine networks experienced a larger loss of nodes than the coarse networks, in part because of the large number of nodes that conform the fine networks.

These results further demonstrated the need to be conscious of the scale and resolution of the data used when modeling species distribution and interactions and how the use of multiple scales may provide more information and result in more robust predictions (Gillespie 2015).

119

Influence of abiotic interactions on network size, connectivity and structure

The host – parasite networks of the potential spread of M. micrantha, extend from the northern coast to the mountainous region of the central part of Puerto Rico. However only 41% of those nodes are invaded by M. micrantha. Surprisingly the addition of other types of biotic interactions into the construction of the potential networks, such as competition, did not constrained the networks and only reduced their size minimally in terms of number of nodes and edges.

In terms of the competition scenarios created, I assumed that M. micrantha would be a bad competitor when first colonizing an already invaded host/region by P. phaseoloides. However, it appears that the constrained distribution of P. phaseoloides in comparison to the distribution of

M. micrantha resulted in limited opportunities for the species to compete. In consequence the effect of competition in the spread of an invasive species observed in my work can change depending on the species and the type of competition modeled with the networks.

High virulence and network structure and connectivity

The rule to only consider as invaded, host with high virulence or a high propagule load reduced both the size and extent of the current and potential spread of M. micrantha in the landscape at both scales. This also showed that the largest concentration of M. micrantha vine patches is located along the northern coast of the island, even though the potential spread networks at both scales show the existence of small groups of suitable hosts along the central and southern regions of the study area. This rule also allowed me to pinpoint which host/regions are most likely to become invaded in the future due to their connection with hosts highly virulent.

120 Surprisingly the introduction of competition to the potential spread networks under the high virulence scenario did not reduced the connectivity of the network. This may imply that the nodes that are being eliminated from the network due to competition with P. phaseoloides are located in the periphery of the network and do not contribute greatly to connectivity and structure of the network.

Perturbation experiments and network connectivity

The fine resolution networks for the potential spread and the competition scenarios have a power-law node degree distribution. This is the distribution that characterizes scale-free networks (Shen et al. 2015). Scale free networks possess a couple of highly connected nodes, often called “hubs”, while the majority of the nodes have a small number of connections. This type of structure makes scale-free networks very vulnerable to directed attacks, such as those that focus on the most connected nodes in the network (Barabási 2009). Scale free networks are also considered vulnerable to disease spread, even to pathogens with low infection rates (Jerger et al.

2007). On the other hand, scale-free networks are resilient against random attacks, or the removal of nodes randomly (Minor and Urban 2008). This explains the effect that the directed removal of nodes in my analysis had in all my networks. The results of my node removal analysis prove the usefulness of networks as a tool to generate information that can direct eradication or conservation efforts working with invasives, endangered species, and even diseases (Urban and Keitt 2001).

I am conscious that the construction of my networks involved a series of educated assumptions that may add a degree of error to the resulting networks. Yet, I believe that the general trends showed in my results provide invaluable information about the influence of biotic

121 interactions on the spread of invasive species, as well as inform about the current state of the spread of M.micrantha in Puerto Rico. In addition, my networks can be easily modified in order to increase or decrease their complexity and similarity with the real world, as more detailed data becomes available

122

CHAPTER FOUR

TABLES

123

Table 4.1 Rank values calculated for each of the land cover Majority (M) classes used in the Susceptibility index. Land cover classes Majority rank High density urban 14 Low density urban 37 Pasture/Agriculture 97 Forest (Age 1) 20 Forest (Age 2) 100 Forest (Age 3) 37 Forest (Age 4) 6 Emergent Wetlands 0 Water 0

124

Table 4.2 Rank values calculated for each of the land cover Variety (V) classes used in the Susceptibility index. Number of land cover classes Variety rank 1 30 2 100 3 91 4 22 5 0 6 0 7 0 8 0 9 0

125 Table 4.3 Rank values calculated for each of the land cover Range (R) classes used in the Susceptibility index. The land cover classes were classifies according to their degree of disturbance, Range of difference in land cover classes Range rank 0 39 1 42 2 100 3 55 4 45 5 15 6 12 7 3 8 0

126

Table 4.4 Results from the binomial linear regression model predicting the presence of vine patches based on the influence of land cover variables. The coefficients of each variable represents the influence each variable has over the susceptibility a habitat to be invaded by vines, and thus were used as the weights on my Susceptibility index. Coefficient Standard error P- value

Intercept -0.954 0.560 0.088

Majority (M) 0.148 0.086 0.085

Variety (V) -0.086 0.246 0.728

Range (R) 0.297 0.128 0.020

127

Table 4.5 Results of the modeling of M. micrantha using both GLMs and GAMs algorithms.

Predictors included in Modeling algorithm Type of variable AIC AUC R2 the model

GLM Single Bio15 248.30 0.91 na

Bio18

AWC

Bulk

CEC

Aspect

Slope

GAM Index Climate PC1 199.90 0.30 0.50

Climate PC2

Slope

128

Table 4.6 All possible types of nodes present in my networks and their associated value of Probability of Propagule Establishment (PPE)

Probability Species Vine of propagule Resolution Node Type Susceptibility probability of patches establishment occurrence (PPE)

Coarse A High High Present 1

B High High Absent 1

C High Low Present 0.4

D High Low Absent 0.4

E Low High Present 0.6

F Low High Absent 0.6

G Low Low Present 0

H Low Low Absent 0

Fine A High High Present 1

B High High Absent 1

C High Low Present 0.4

D High Low Absent 0.4

E Medium High Present 0.8

F Medium High Absent 0.8

G Medium Low Present 0.2

H Medium Low Absent 0.2

I Low High Present 0.6

J Low High Absent 0.6

K Low Low Present 0

L Low Low Absent 0

129 Table 4.7 Adjacency matrix for the host-parasite interaction scenario of the current spread of M. micrantha. The table in the left was used to generate the coarse networks, while the table in the right was used to generate the fine networks. Only values ≥0.5 resulted in connections between node Node Node A B C D E F G H A B C D E F G H I J K L type type A 1.0 0.0 0.7 0.0 0.8 0.0 0.5 0.0 A 1.0 0.0 0.7 0.0 0.9 0.0 0.6 0.0 0.8 0.0 0.5 0.0 B - 0.0 0.0 0.0 0.0 0.0 0.0 0.0 B - 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 C - - 0.4 0.0 0.5 0.0 0.2 0.0 C - - 0.4 0.0 0.6 0.0 0.3 0.0 0.5 0.0 0.2 0.0 D - - - 0.0 0.0 0.0 0.0 0.0 D - - - 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 E - - - - 0.6 0.0 0.3 0.0 E - - - - 0.8 0.0 0.5 0.0 0.7 0.0 0.4 0.0 F - - - - - 0.0 0.0 0.0 F - - - - - 0.0 0.0 0.0 0.0 0.0 0.0 0.0 G ------0.0 0.0 G ------0.2 0.0 0.4 0.0 0.1 0.0 H ------0.0 H ------0.0 0.0 0.0 0.0 0.0 I ------I ------0.6 0.0 0.3 0.0 J ------J ------0.0 0.0 0.0 K ------K ------0 0 L ------L ------0

130 Table 4.8 Adjacency matrix for the host-parasite interaction scenario of potential spread of M. micrantha. The table in the left was used to generate the coarse networks, while the table in the right was used to generate the fine networks. Only values ≥0.5 resulted in connections between nodes. Node Node A B C D E F G H A B C D E F G H I J K L type type A 1.0 1.0 0.7 0.7 0.8 0.8 0.5 0.5 A 1.0 1.0 0.7 0.7 0.9 0.9 0.6 0.6 0.8 0.8 0.5 0.5 B - 1.0 0.7 0.7 0.8 0.8 0.5 0.5 B - 1.0 0.7 0.7 0.9 0.9 0.6 0.6 0.8 0.8 0.5 0.5 C - - 0.4 0.4 0.5 0.5 0.2 0.2 C - - 0.4 0.4 0.6 0.6 0.3 0.3 0.5 0.5 0.2 0.2 D - - - 0.4 0.5 0.5 0.2 0.2 D - - - 0.4 0.6 0.6 0.3 0.3 0.5 0.5 0.2 0.2 E - - - - 0.6 0.6 0.3 0.3 E - - - - 0.8 0.8 0.5 0.5 0.7 0.7 0.4 0.4 F - - - - - 0.6 0.3 0.3 F - - - - - 0.8 0.5 0.5 0.7 0.7 0.4 0.4 G ------0.0 0.0 G ------0.2 0.2 0.4 0.4 0.1 0.1 H ------0.0 H ------0.2 0.4 0.4 0.1 0.1 I ------I ------0.6 0.6 0.3 0.3 J ------J ------0.6 0.3 0.3 K ------K ------0.0 0.0 L ------L ------0.0

131 Table 4.9. Characteristics of the different networks of the different scenarios of the spread of M. michantra Resolution Presence/Absence High Virulence Coarse Network metrics Current Potential Competition Current Potential Competition Size (number of nodes) 1641 2251 2236 35 489 427 Edges 2228 3312 3252 46 352 313 Components 86 4 4 1 173 149 Size of largest component 1364 2240 2225 35 44 41 Mean clustering coefficient 0.80 0.82 0.82 0.42 0.39 0.40 2.72 (± 2.94 (± 2.91 (± 2.63 (± 1.44 (± Mean node degree (± SD) 14.51) 18.22) 18.27) 2.31) 1.02) 1.47 (± 1.03) Diameter 8 10 10 6 8 8 Mean betweenness 1332.00 3102.32 3071.96 39.68 5.71 5.62 Density 2.00E-03 1.00E-03 1.30E-03 7.73E-02 2.95E-03 8.74E-03 Fine Size (number of nodes) 3014 5671 5585 112 2761 2591 Edges 5853 12246 11964 119 2662 2519 Components 10 22 25 6 663 616 Size of largest component 2988 5598 5514 86 141 124 Mean clustering coefficient 0.70 0.70 0.70 0.11 0.443 0.452 3.88 (± 4.32 (± 4.28 (± 2.12 (± 1.93 (± Mean node degree (± SD) 11.25) 14.62) 14.72) 2.00) 1.38) 1.94 (± 1.38) Diameter 15 17 17 14 16 15 Mean betweenness 6430.12 12279.16 12066.250 149.27 25.03 20.96 Density 1.29E-03 7.62E-04 7.67E-04 1.91E-02 6.99E-04 7.51E-04

132 Table 4.10 Characteristics of the networks generated after the random or directed elimination of nodes. Potential Competition Resolution Variables Random Directed Random Directed Size (number of nodes) 2027 2027 2015 2015 Mean clustering coefficient 0.82 0.46 0.82 0.43 Mean node degree (± SD) 2.72 (±17.05) 0.404 (±0.75) 2.72 (±17.04) 0.37 (±0.70) Coarse Diameter 12 6 11 6 Mean betweenness 2576.48 0.19 2545.79 0.15 Density 1.34E-03 1.99E-04 1.35E-03 1.84E-04 Size (number of nodes) 5152 5152 5068 5068 Mean clustering coefficient 0.70 0.48 0.70 0.48 Mean node degree (± SD) 3.92 (± 13.90) 1.54 (±1.60) 3.95 (±13.88) 1.52 (±1.60) Fine Diameter 20 18 25 19 Mean betweenness 10945.59 11.326 11032.66 11.070 Density 7.69E-04 2.99E-04 7.80E-04 2.99E-04

133

CHAPTER FOUR FIGURE LEGENDS

134

Figure 4.1 Diagram explaining the construction of a complex network. (a) Example of a landscape with vine patches and different levels of susceptibility to vine invasions. (b) Example of my complex network, in which each node represents a host/region and includes information about its susceptibility to vine patch invasion, the presence of vine patches, and the probability of occurrence of a vine species. In this network, black nodes represent areas where the species being modeled is present, while light blue nodes are host that can potentially be invaded by the species. (c) Diagram depicting the steps and processes involved in the generation of a complex network. It starts with the incorporation of the susceptibility data that is going to characterize the nodes of the network. Then the abiotic data that determine the optimal conditions for the species being modeled is included in the form of a species distribution map, followed by a vine patch map. All this information is incorporated into the table of attributes of the polygon layer that represents the hosts/regions map. (d) Afterwards, this information is used to classify all the different types of host/regions (nodes) in the network.

Figure 4.2 Maps showing the spatial distribution of the coarse and fine networks of the actual and potential spread of M. micrantha constructed under the presence/absence virulence scenario.

The size of the nodes represents its degree.

Figure 4.3 Maps showing the spatial distribution of the coarse and fine networks of the actual and potential spread of M. micrantha constructed under the high virulence scenario. The size of the nodes represents its degree

135 Figure 4.4 Maps showing the spatial distribution of the twelve spread networks of M. micrantha constructed.

Figure 4.5 Scatter plots showing the degree distribution of nodes in each of the networks: The host-parasite current spread (a and d), potential spread (b and e) and competition (c and f) networks with coarse, and fine resolution, respectively. Black dots represent the real data, while the dashed line shows a fitted power law distribution. The resulting P-values of a power law goodness-of-fit test using a bootstrapping procedure are shown in the graph. P-values <0.05 means that the power law model does not provide a plausible fit to the real data.

Figure 4.6. Scatter plots showing the degree distribution of nodes in each of the networks under the high virulence scenario: The host-parasite current spread (a and d), potential spread (b and e) and competition (c and f) networks with coarse, and fine resolution, respectively. Black dots represent the real data, while the dashed line shows a fitted power law distribution. The resulting

P-values of a power law goodness-of-fit test using a bootstrapping procedure are shown in the graph. P-values <0.05 means that the power law model does not provide a plausible fit to the real data.

Figure 4.7 Bar plots comparing the (a and d) number of edges, (b and e) components and (c and f) the size of the largest component of the spread networks of M. micrantha after removing 10% of the nodes through a random and directed process. Plots (a-c) correspond to the coarse networks, while plots (d-f) correspond to the fine networks.

136 FIGURE 4.1

a) Landscape with vine patches b) Complex landscape network

Edge Component

Node

c) Data preparation for complex landscape network d) Construction of complex landscape network

i a 001

p x High Medium Low

n 002 003

s i

a a High Low

o >0.50 <0.50

Present Absent

Host Host SI Species Patch ID area probability number 001 145800 2 0.45 3 002 64800 3 0.75 1 003 129600 1 0.90 1

137 FIGURE 4.2

Coarse resolution Fine resolution

Current spread

Potential spread

138 FIGURE 4.3

Coarse resolution Fine resolution

Current spread

Potential spread

139 FIGURE 4.4

Vine presence

High virulence

140

FIGURE 4.5

(a) (b) (c)

(d) (e) (f)

141 FIGURE 4.6

(a) (b) (c)

(d) (e) (f)

142 0 0 0

0 0 0

4 2 3 FIGURE 4.7 Host−parasite

0 Competition

)

0 0

e 0 0

0 5

3 1

0 0 0

(

0 0 0

4 2 3

2 n

p Host−parasite

f 0 0 0

0 Competition

o 0 0 0

0 0 5

)

2 1 1

0 0

0 5

3 1

(

o 1

f 0 0 0

o 0 0 0

0 0 5

2 1 1

0 0 0

o 1

0 0 0

0 0

3 6

0 0

5 0

)

2 5

0 0

(

0 0

3 6

2 4

0 0

f 0 0

0 0

o 0 0

5 0

)

2 5

1 3

0 0

(

0 0

2 4

1 o 2

f 0 0

o 0 0

5 0

1 3

0 0

0 0 0

0 0

1 o 2

5 No removal Random Directed No removal Random Directed z No removal Random Directed

0 0 0

No removal Random Directed No removal Random Directed No removal Random Directed

143

CHAPTER FIVE

GENERAL CONCLUSIONS

144 Most studies on invasive vines focus on individual species of alien origin, thus ignoring the fast proliferation of native vines, which are increasing in abundance alongside their alien counterparts, Here, I made the decision of grouping together proliferating vine species, indifferent of their origin. This decision was not only supported by my field observations, but also by my statistical results, which show that proliferating vines both native and alien, share similar traits. This also allowed me to generate a more accurate classification model that can predict vine species proliferation status based only on five plant traits. The simplicity if this classification model allows it to be useful for the creation of assessment plans for determining which species should or should not be introduced into the island, as well as for monitoring species with a high risk to become proliferating species. Furthermore, my classification model approach can be applied to other functional groups of plants in an effort to limit the introduction of potential invaders, monitor alien and native species likely to proliferate, and manage landscapes invaded by alien species.

In addition, I decided to take a community approach to understand the influence of both abiotic and biotic factors in the assembly and composition of vine patches. Here my results demonstrated that even though a vine community diversity and composition is influence by a combination of climatic, edaphic, topographic and land cover variables, extreme climatic variables have the most influence. On the other hand, land cover variables only had a weak influence on community composition, even though vines due are structural parasites that depend on host or trellis availability to grow and establish a population. This may be due to some vine species ability to overcome the limitations posed by host availability by using other vine species for support. All of which, only highlights the need to examine the multiple types of interactions

145 that may be occurring inside these vine communities, information that can give us an insight on the dynamics within vine meta-communities.

Acknowledging the important role that abiotic interactions play in the assemblage and expansion of vine communities, I decided that any approach to model the invasion process of an invasive or proliferating vine should incorporate these interactions. This motivated the construction of a complex network that modeled the connectivity of vine’s hosts/regions for the invasion of an invasive vine. The novel thing of this approach was its incorporation of two networks in one (i.e., the host and the vine patch distribution networks), without increasing the complexity of analyzing these networks. My results demonstrated that the incorporation of different types of biotic interactions and different scales had an effect on the size, if not in the structure and connectivity of the networks. However this results can vary depending on the type of biotic interactions and the species that are interacting.

In terms of scale, even though my results vary across scales the general patterns observed remained the same. When we examined the influence of environmental and land cover variables on community composition, the changes on scale resulted in the inclusion of new variables, but these only showed weak influences on vine composition. In the case of my complex networks, changes in data resolution affected the size of the resulting networks, but across the different biotic interactions scenarios, the general effects on size, structure and connectivity of the network remained the same. This demonstrates that the use of coarse data can also reveal important patterns when modeling invasion, an important finding because high-resolution data for large-scale modeling is not often available or affordable.

In conclusion, this study helped improve our understanding of the dynamics of vine invasions and the factors influencing them both at the community and at the landscape level. Also the

146 approaches generated in the coarse of this dissertation have great management applicability and can extent their application to other functional groups of plants.

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