Testing Ecoregion Limits with Woody Versus Herbaceous Taxa: Are Ecoregions the Same for Different Growth Forms?

Testing ecoregion limits with woody versus herbaceous taxa: Are ecoregions the same for different growth forms?

Kelsey M. Dix

Royal Botanic Garden Edinburgh, 20 Inverleith Row, Edinburgh EH3 5LR, UK

Thesis submitted in partial fulfillment of the requirement for the degree of MSc in the Biodiversity and Taxonomy of Plants 2

“Quite obviously, the clarity of the boundaries between the floral kingdoms (and between the phytochoria at lower levels of Takhtajan’s system) is variable, depending on the nature of the barrier between them and on the history of their floras.”

– Cox 2001, p. 521

“Following Wallace, we might stipulate that floral Kingdoms, like zoogeographic regions, must be areas of similar size, compact, and easily defined.”

- Cox 2001, p. 520

Abstract Ecoregions are thought to be useful to understand patterns of species diversity across space and time. In principle, they are relatively uniform geographic areas in terms of biotic composition, often used as baseline units in biogeographical regionalization, and thus in ecological, evolutionary and global change research. This study tests the existence of ecoregions as floristic units, delineated by boundaries that reflect spatial discontinuities in species composition. It also examines the extent to which herbaceous and woody plants form similar ecoregions. Using the known flora of Nicaragua as a case study, both taxonomic and phylogenetic species turnover metrics are utilized to test for boundaries delineating ecoregions. Results from categorical wombling, a boundary detection method designed for depicting zones of rapid change across space, demonstrate that ecoregions do exist for both growth forms and that boundaries are greater and more efficent for woody taxa. Thus, ecoregions exhibit different patterns for herbs and trees, potentially due to niche conservatism and dispersal limitations.

Keywords: Ecoregions, categorical wombling, Nicaragua, boundary detection, biogeography, beta-diversity

Acknowledgements Supervisors Dr. Tiina Särkinen and Dr. Iván Jiménez gave superb guidance in grasping the methods and results. Dr. Adam Smith contributed to the comprehension of species distribution modeling and offered feedback on initial results. Species distribution modeling was conducted by Smith, the flora phylogeny was completed by Särkinen and premliminary R scripts written by Jiménez. Olga Martha Montiel and Doug Stevens shared vital information on Nicaragua and its flora. Dr. Peter Stevens, who is quoted “the universe is inappropriate”, aided in evaluating and resolving phylogenetic topologies. Charlotte Taylor and Peter Moonlight supplied Rubiaceae and Begoniaceae phylogenetic affiliations, respectively. The Royal Society International Exchange Scheme provided funding for travel to Missouri Botanic Garden. Russ and Shelley Dix donated time and wordsmith knowledge for betterment of this thesis.

Contents Abstract...... 3

Acknowledgements...... 4

Contents...... 5

List of Figures ...... 7

List of Tables ...... 8

1 Introduction ...... 9

1.1 Ecoregions: Definition and debate ...... 9

1.2 History, importance, and application of ecoregions ..... 11

1.1.1 History ...... 12

1.1.2 Importance and application ...... 14

1.3 Evaluating ecoregions ...... 16

1.4 Available methods for exploring ecoregions ...... 18

1.5 Effect of life history ...... 22

1.6 Study aims and hypotheses ...... 24

2 Methods ...... 25

2.1 Flora of Nicaragua ...... 25

2.2 Species occurrence data ...... 28

2.3 Species Distribution Modeling ...... 29

2.4 Flora phylogeny ...... 35

2.4.1 Phylogenetic tree estimation ...... 35

2.4.2 Time calibration ...... 36

2.5 Analysis of representativeness ...... 39

2.5.1 Taxonomic ...... 39

2.5.2 Growth form ...... 41

2.6 Categorical wombling ...... 46 6

2.6.1 Measuring β-diversity ...... 46

2.6.2 Categorical wombling method ...... 49

2.6.3 Null models ...... 51

2.6.4 Significance testing ...... 53

3 Results ...... 56

3.1 Categorical wombling ...... 56

3.1.1 Identifying ecoregions for growth forms ...... 59

3.1.2 Identifying differences in ecoregions ...... 59

4 Discussion ...... 69

4.1 General findings ...... 69

4.2 Why woody species show stronger ecoregions? ...... 71

4.3 Study limitations and strengths ...... 73

4.4 Implications and further research ...... 76

5 Conclusion ...... 79

6 References ...... 81

Appendix 1...... 104

List of Figures Figure 1...... 14 Figure 2...... 26 Figure 3...... 32 Figure 4...... 33 Figure 5...... 37 Figure 6...... 42 Figure 7...... 43 Figure 8...... 44 Figure 9...... 45 Figure 10...... 48 Figure 11...... 51 Figure 12...... 53 Figure 13...... 57 Figure 14...... 58 Figure 15...... 61 Figure 16...... 62 Figure 17...... 63 Figure 18...... 65 Figure 19...... 66 Figure 20...... 67 Figure 21...... 68 Figure 22...... 78

List of Tables Table 1...... 10 Table 2...... 21 Table 3...... 25 Table 4...... 27 Table 5...... 29 Table 6...... 29 Table 7...... 31 Table 8...... 34 Table 9...... 38 Table 10...... 39 Table 11...... 39 Table 12...... 40 Table 13...... 42 Table 14...... 42 Table 15...... 64 Table 16...... 64

1 Introduction

1.1 Ecoregions: Definition and debate The widely accepted idea that the world consists of regions with differing biological structures has profoundly influenced how humanity comprehends and relates to natural landscapes (Whittaker et al., 2005). Ecoregions are categorical biogeographic divisions that have been used in science and conservation to characterize spatial variation in biotic composition at broad geographic scales (e.g., Olson et al., 2001). Ecoregions can be seen as baseline units of broad-scale biogeographic categorizations such as biogeographic realms (Holt et al., 2013) and floristic regions (Takhtajan, 1986). Definitions of ecoregions and biogeographic divisions vary between and within scientific fields, with inconsistencies in their categorization and partition (Table 1; Donoghue and Edwards, 2014; Hughes et al., 2013; Kent et al., 2006; Mackey et al., 2007). More recently, studies have aimed at developing biogeographic units that consider both ecological and biogeographic factors and conside the possibility that such units constitue evolutionary meta-communities through time (Hughes et al., 2013; Särkinen et al., 2011).

The term ecoregion was initially introduced for land use management (Bailey, 1983, 1986, 1996; U.S. Forest Service, 1993), but has developed into a well-established concept across biology. Since the publication of one of the most influential biogeographic regionalizations by WWF in 2001, ecoregions have become a commonly used concept in biological sciences both in the fields of ecology and evolution. They continue also to be used widely in conservation and policy implementation. Not only does ecoregional classification guide conservation efforts 10

(Olson & Dinerstein, 2002; Olson et al., 2001; Wikramanayake et al., 2002; Dasmann, 1972), but they are used as the foundation of evolutionary and ecological research (Salguero-Gómez et al., 2016, Kier et al., 2009; Fritz et al., 2009; Loarie et al., 2009; Antonelli et al., 2015) and are used as macroecological units in studies exploring the effects of climate and land use change on biodiversity and ecosystem services in the global change science field (Loarie et al., 2014; Naidoo et al., 2008; González-Orozco et al., 2014; Feeley and Silman, 2010).

Table 1. Glossary of terms for common ecoregion terminology, adopted from Gonzalez-Orozco et al. (2014). Definitions vary in inclusion of integrated climate variables, vegetation structure and/or presence of key lineages.

Term name Description Authors Ecoregion Categorical biogeographic division Olson et al. 2001 characterizing spatial variation in biotic composition at broad geographic scales that depict natural units within which ecological movement and linking processes are preserved Biogeographical Geographical area with distinct biotic Holt et al. (2013a) divisions composition based on species’ Kreft & Jetz (2013) (Bioregion, distributions and phylogenetic phytogeographical or relatedness zoological regions) Biome Geographical area defined by similar Gonzalez-Orozco et al. (Bioclimatic zone) climatic conditions and similarity in (2014) biota in terms of their ecological adaptations (e.g. succulent, mesic, arid) Biome (Evolutionary Geographical areas that act as Pennington et al. (2009) meta-community) evolutionary meta-communities with shared Särkinen et al. (2012) lineages and high floristic similarity at Hughes et al. (2012) different taxonomic levels, corresponding Donohgue et al. (2008) to areas with shared climatic factors and vegetation physiognomy Area Distribution of any taxonomic unit Wulff (1950) Biota Combined distribution of a group of taxa Parenti and Ebach (2009) that occupy a common geographical area Biotic Area Geographical areas inhabited by a biota. Parenti and Ebach (2009) Limits of taxon distribution specify limits of the area. Endemic Area Geographical area to which a taxon or Parenti and Ebach (2009) biota is understood to be native Vegetative Geographical area defined by a particular Takhtajan (1986) (Floristic) Zone type of vegetation (e.g. savannah, tundra) 11

Current debate is over not only how to define and map ecoregions or biogeographic divisions, but also whether such units are real in space. Several authors have called for studies that look for natural breaks in species distributions across organisms to test for the validity of ecoregions in nature because community similarity between sites decreases with distance (Magnusson, 2004; Williams, 1996; Williams et al., 1999). Such calls reflect the deep-rooted debate about the existence and nature of plant assemblages as naturally occurring spatially defined entities. Some ecologist argue that biogeographic units are artificial constructs (Callaway, 1997; Hoagland and Collins, 1997; Kent et al., 1997; Choesin and Boerner, 2002; Lortie et al., 2004; Austin, 1999a, 1999b, 2002; Mackey et al., 2007). However, the concept of ecoregions may be tinted by human preconceptions about nature and the rush to achieve a pragmatic categorization of landscapes (Magnusson, 2004; Kent et al., 2006). Categories, like ecoregions, become “self-fulfilling prophecies” when their existence is assumed instead of tested (Magnusson, 2004). Below is an overview of the history, importance and applications of ecoregions and an outline the basic aims of this thesis that focuses on exploring ecoregions in space.

1.2 History, importance, and application of ecoregions Due to the importance of ecoregions in categorizing biodiversity patterns, how we delimit and map ecoregions is crucial for continental-scale macroevolutionary and biogeographic studies (Hughes et al., 2013; González-Orozco et al., 2014). Practicality of ecoregions is vital to their application and effectiveness should not be sacrificed for efficiency, otherwise classification undermines dissimilarity. Vegetation acts as an ecosystem engineer, forming habitats for fauna (Linder et al., 12

2012), and therefore plants are the most appropriate biological kingdom to determine ecoregions.

1.1.1 History Ecoregions are perhaps a somewhat novel concept in conservation biology, but they are based on traditional biogeography (Wikramanayake et al., 2002). The concept of biogeographic regions came about with the European expansion into the tropics from the 16th century onward and the eagerness to grasp nature’s patterns of diversity and richness for religious, scholarly and economic pursuits (Lomolino et al., 2004, Kreft and Jetz 2010; Whittaker et al., 2005). Georges-Louis Buffon first observed in 1761 that the large mammals of the Old World and New World tropics were rather different. Alexander von Humboldt (1816, 1820) found that “Buffon’s law” (Nelson, 1978) also applied to birds, reptiles, insects, spiders and flowering plants. Augustin de Candolle developed the first world-wide floristic biogeography system in 1820. He simply defined 20 “areas of endemism” that were each characterized by numerous plants species bound by natural barriers (i.e. ocean, desert or temperature change) or the presence of competing species (Cox, 2001). Victorian-era vegetation maps designated floras by their climatic differences because the historical aspect to regional flora development was only recognized after acceptance of Darwin’s theory of evolution (Cox, 2001). Adolf Engler’s (1879, 1882) four “realms” were formed on de Candolle’s climatic and physiological criteria with an attempt to ascertain floral geologic history and these were later subdivided by Ludwig Diels (1908) and Ronald Good (1947, 1953, 1964, 1974). Armen Takhtajan (1978, 1986) expanded previous systems by defining six “floristic kingdoms” with the assumption that each floral unit’s distinctiveness was the outcome of isolation, which granted 13 independent evolution and incorporated criteria for differentiating biogeographical divisions at levels of hierarchical endemicity.

Zoogeography developed separately. Wallace (1876) devised the basal definition for biogeographical regions still currently used in zoogeography and proposed that only endemism, above generic level is useful for recognizing these regions. Nelson & Platnick in 1981 noted that the 21 Wallace zoogeographic subregions corresponded to de Candolle’s (1820, 1838) regions and the condensed realms/kingdoms of Engler (1879, 1882), Diels (1908), Good (1947-1974) and Takhtajan (1978, 1986). Most recently, Holt et al. (2013a) revised Wallace’s zoogeographic regions using distribution data from mammals, birds and amphibians.

Over a century passed before the modern view of biogeographic regions could be realized which occurred after the acceptance of plate tectonics in the 1960s (Cox, 2001). Simultaneously, the conservation community gained widespread support in establishing a worldwide network of natural reserves that encompassed representative areas of the world’s ecosystems (Whittaker et al., 2005). Sponsored by the IUCN, Dasmann (1972, 1973) and Udvardy (1975) applied the “biogeographic representation principle” to expansion and combination of earlier faunal and vegetation maps to produce a nested hierarchy of biological realms (Whittaker et al., 2005). The Dasmann-Udvardy framework subdivides the continents into faunal regions (biotic realms) where a biome classification system is applied and biotic provinces are defined by subdividing a physiognomically delineated climax vegetation type based on a distinctive fauna (Whittaker et al., 2005). Regions with less than 65% of species in common are considered separate faunal provinces, causing this 14 zonal scheme to recognize azonal features (i.e. high mountains and mountainous islands) (Whittaker et al., 2005). Bailey (1996, 1998) and Omernik (1987, 1995) developed similar hierarchical classification systems that concentrated on ecosystem configuration characteristics instead of taxonomic differentiation like Dasmann-Udvardy (Omernik and Bailey, 1997; Jepson and Whittaker, 2002). The most prominent global scheme is the WWF’s Ecoregions map (Figure 1; Olson et al., 2001; Dinerstein et al., 1995). The structure includes biogeography, habitat type and elevation data to distinguish finer-scaled biogeographic units and aims to depict natural units within which ecological movement and linking processes are preserved (Olson et al., 2001).

Figure 1. WWF ecoregional classifications (Olson et al. 2001).

1.1.2 Importance and application

Conservation initiatives Ecoregions are seen as useful tools in conservation initiatives, because they may be used to divide large areas into smaller 15 units for land management, and for setting conservation priorities (Margules and Pressey, 2000). They may help effective fundraising in conservation (Whittaker et al., 2005). The WWF ecoregion scheme published in 2001 by Olson et al. attempted to transition from theory to applied ecology and aid in mobilizing real conservation action but it would be naïve to assume this scheme is the definitive answer to land-use planning and designation (cf. Meir et al., 2004). Yet, ecoregions are now utilized by the leading international non-legislative institutions to identify conservation priorities (Wikramanayake et al., 2002).

Policy implementation Ecoregions or ecoregion-like biogeographic units are also used in policy implementation. One such example is the largely questionable Brazilian Forest Code (Act 4.771/1965). Brazil has six distinct major “biomes” that affect the size and percentage of land protected by Legal Reserves Areas (Sluter and de Mendonça, 2009; Granziera and Rei, 2013). The percentage of allowed land use change per biome is hence radically different, varying from 0 to 40% across Brazil (Sluter and de Mendonça, 2009; Granziera and Rei, 2013).

Basic and applied sciences Ecoregions also play an important role in basic and applied sciences. Ecoregions are used as baseline study units in many fields of biology, ecology and Earth System sciences to test various hypotheses, ranging from understanding correlates of extinction risk in mammals (Fritz et al., 2009) to identifying climate change impacts in the next coming decades (Loarie et al., 2009). Addressing ecological questions often characterization of change in variables across space and time (Turner and Gardner, 1991; Wiens et al., 1985; Wiens, 1995). 16

Vegetation boundaries can be the result of environmental variable gradients or disturbances (natural or human-related), which change the distribution of some ecological variable over an area (Hansen and diCastri, 1992; Johnston et al., 1992; Fortin and Drapeau, 1995; Fortin et al., 1996). Vegetation boundaries maps have many functions, including understanding climate or land use change, pattern analysis, relationships among spatial variables and faunal habitat maps (Hall and Maruca, 2001).

1.3 Evaluating ecoregions Despite their integral role in both basic and applied sciences, evaluation of ecoregions has gained little attention. Assorted depictions of ecoregions or ecoregion-like units have been projected at global and continental scales (e.g., Olson and Dinerstein, 2002; Morrone, 2001; Olson et al., 2001; Cracraft, 1994; McLaughlin, 1992; Udvardy, 1975; Dasmann, 1974). Some maps, including the most popular WWF Ecoregion map (Olson et al., 2001), have shown poor predictive performance, largely due to poor spatial resolution but also possibly because these maps were not based on readily repeatable methods and explicit data sources (Särkinen et al., 2011; Jepson and Whittaker, 2002). Some of these issues are now being addressed with the development of innovative methods based digitized species distribution data instead of using specialist collaborations of biogeographers, taxonomists, conservation biologists and ecologists (Wikramanayake et al., 2012). These make ecoregion mapping into a repeatable, data-driven science (Vilhena and Antonelli, 2015; Faber-Langendoen et al., 2014; Oliveiro et al., 2013; Holt et al., 2013a; Mouillot et al., 2013; Linder et al., 2012; Kreft and Jetz, 2010; Mackey et al., 2008). 17

The standard biological issue when trying to catalog biotas is that, across spatial scales, changes in species assemblages’ composition “hovers in a tantalizing manner between the continuous and discontinuous” (Webb, 1954; Williams et al., 1999). Biologists are accustomed to complex and often continuous variation in fundamental systems because the precise essence of defining taxa inevitably fluctuates from phylum to phylum and depending on species concept used (Cox, 2001). The debate of ecoregions is relatable to disputes in vegetation ecology between Clementsian (Clements, 1916, 1939) and Gleasonian (Gleason, 1926, 1939) assemblage opinions and debates of species concepts in taxonomy and systematics because “discrete entities may be practically self-defining, but a continuous variable, in space or in time, resist internal partition” (de Queiroz, 2007; Womble, 1951).

It is hence vital to critically study the concept of ecoregions by testing whether ecoregion-like units exist in nature to justify their use as baseline units in both conservation and basic and applied sciences (Fortin and Dale, 2014; Stuart et al., 2012). The existence of ecoregions implies relatively homogeneous areas of species composition, delimited from their neighbors by borders. Borders are places characterized by high turnover, where more rapid rates of change would lead to clearer borders (González-Orozco et al., 2014; Cox, 2001). Absence of ecoregions would show that differences in biotic somposition between localities would be explained by the decay of biotic similarity over geographic distance (Soininen et al., 2007, Baselga, 2007; Kent et al., 2006). It is also possible that some boundaries exist, but do not depict ecoregions because they occur disocntinously across space due to sharp, abrupt landscape changes, remembering that numerous biotically homogeneous units 18 indicate boundaries, but not vice versa (Fortin and Dale, 2014; Oden et al. 1993).

1.4 Available methods for exploring ecoregions The problem of how ecoregions or similar biogeographic units can be divided into two inter-related questions: (a) how to define the contents of the regions, and (b) how to define their boundaries (Cox, 2001). Different biotic regions are generally easy to recognize (i.e., rainforest from tundra) but difficulty arises in construing the exact borders of such units and their definitions (Donoghue and Edwards, 2014). In fact, many authors of previous biogeographical classifications have acknowledged this fact in stating that despite the clear black lines depicted in most categorical ecoregion maps, the clarity of boundaries between ecoregion-like units depends on the nature of the barrier and floral history (Cox, 2001; Takhtajan, 1986, Holt et al., 2013a; Donoghue and Edwards, 2014; Mackey et al., 2007; May, 1986).

There are two principal ways to partition an area spatially: (a) gathering adjacent localities that have analogous values of the indicatory variable(s) by creating spatial homogeneous clusters using clustering algorithms, or (b) apportioning the entire area into subregions, centered on degree of dissimilarity, by outlining boundaries between unlike areas with edge detecting techniques (Dale and Fortin, 2014; Fortin and Drapeau, 1995).

Thus far, clustering and ordination methods have been used most widely in mapping ecoregions. Clustering and ordination can be applied to diverse types of data (quantitative or qualitative, univariate or multivariate) to divide a study region into patches (Fagan et al., 2003). Spatial contiguity restrictions must be implemented into clustering algorithms in order to 19 create spatially homogeneous clusters and thereby, as a by- product, boundaries are formed between divergent sampling sites (Fortin and Drapeau, 1995). A major setback is only clear, sharp boundaries can be described, which may not reflect reality, and exact boundary locations are still vague (Fagan et al., 2003). In addition, the subjectivity of choosing a degree of similarity for cluster creation and the “choice of an adequate number of clusters” is a long-standing concern (Kreft and Jetz, 2010; Dale and Fortin, 2014).

Despite the issues outline above, clustering methods have remained the most widely used methods in ecoregion science (Table 2). It could be argued, however, that boundary detection methods are the most appropriate methodology for exploring the presence and nature of boundaries because they focus on identifying regions with rapid rates of spatial change in vegetation and related abiotic/biotic conditions (Jacquez et al., 2000; Hall and Maruca, 2001; Kent et al., 2006). Adjacent locations with high spatial rates of change (e.g. species turnover) suggest boundaries (Fortin, 1994; Legendre and Legendre, 1998; Fortin and Drapeau, 1995). Such methods have been rarely used, in papers exploring ecoregions, and only a set of references can be found where these methods have been applied (Camarero et al., 2000; Fortin, 1997; Kent et al., 2006)

The history of boundary detection methods in relation to vegetation studies goes back to Van der Maarel (1976) who led the development of quantitative plant ecology methods for describing community boundaries. He presented the notion of boundary detection between floristic assemblages and proposed preliminary analytical methods for linear transects oriented at right angles across the boundary or transition zone (Kent et al., 2006). Modern approaches focus on describing spatial rates 20 of change in community and ecosystem variables and identifying sectors of greatest changes (Barujani et al., 1989). Edge detection algorithms provide further boundary information (width, shape and intensity) because they use a partitioning approach to detect tracts that demonstrate the most abrupt shifts amid neighboring locations, as well as within landscape mosaics (Fortin, 1994; Fagan et al., 2003; Fortin and Drapeau, 1995; Fortin et al., 2000, Jacquez et al., 2000). Boundary detectors for two-dimensional area data compute difference among neighboring localities with either moving windows or kernel filters (Dale and Fortin, 2014). Moving windows calculate a metric from adjacent location values that quantifies the degree of difference among the four values, while kernel filters compute a centrally assigned summation of multiplied values of each cell and its correspondent location in the kernel (Dale and Fortin, 2014).

Wombling methods, orginially described by Womble (1951), are most relevant to ecoregion science because they can integrate data from multiple variables, rather than a single variable as in kernel detectors and wavelet approaches (Fagan et al., 2003; Jacquez et al., 2000). Lattice wombling calculates local rates of changes, either absolute differences or gradients, among adjacent sampling cells that designated to the center of a “kernel” (i.e. a window of n x n cells), which can be sensitive to local noise between adjacent sites leading to artificial boundaries (Csillag et al., 2001). Sometimes, quantitative data is sampled with an irregular spaced design (e.g., random, stratified, systematic triangular), thus traditional square kernels are inadequate and triangular wombling, based on a triangular kernel, can be applied (Fagan et al., 2003). Trios of sampling sites are obtained with a Delaunay network that links 21 adjacent units into triangles (Fortin and Drapeau, 1995). Lastly, when data is categorical, boundaries can be detected using categorical wombling as a “mismatch” of categories between two adjacent sites, discussed in detail later (Fortin and Drapaeu, 1995; Jacquez et al., 2000, Fagan et al., 2003). This analysis performs better when several categorical variables are available because boundaries are gauged where multiple mismatches between adjacent sites occur (Fagan et al., 2003). In order to discriminate between cohesive, connected boundaries and those that are disconnected, scattered and occur by random chance, randomization procedures can be utilized (Fagan et al., 2003). Randomization procedures should be done with caution as regions and boundaries are spatial auto-correlated (Fagan et al., 2003; Kent et al., 2006).

Table 2. Various clustering methods and the authors utilized them to published biogeographic ecoregionalization studies.

Clustering method Acronym Authors Unweighted pair-group method using UPGMA Zhang et al. (2016), Linder arithmetic averages et al. (2012), Kreft and Jetz (2010), Holt et al. (2013a) Unweighted pair-group method using UPGMC Kreft and Jetz (2010) centroids Weighted pair-group method using WPGMA González-Orozco et al. arithmetic averages (2014), Kreft and Jetz (2010) Weighted pair-group method using WPGMC Kreft and Jetz (2010) centroids Divisive polythetic clustering Williams (1996), Williams et (TWINSPAN) al. (1999) Network clustering Vilhena and Antonelli (2014) Hierarchical clustering Neves et al. (2015) Spatial constrained clustering Hall and Maruca (2001) Factor analysis ordination McLaughlin (1992) Decorana ordination Williams et al. (1999) Non-metric multidimensional scaling NDMS Zhang et al. (2016), Linder ordination et al. (2012), Kreft and Jetz (2010), Holt et al. (2013a)

1.5 Effect of life history Another imperative query, if ecoregions are found to exist in nature, is whether these biogeographic divisions are the similar for different organisms.

Some studies compared ecoregions defined using distributional data from different organisms. Holt et al. (2013a) examined three distinct vertebrate classes using separate phylogenetic trees and found variation in spatial patterns among them perhaps due to differences in dispersal ability and sensitivity to environmental conditions. Linder et al. (2012) found regionalization for African birds, mammals, reptiles and plants to be in partly congruent. This congruence could be attributed to vertebrate distributions being influenced by vegetation (predicted by Rueda et al., 2010), mutual responses to similar climatic factors or a shared fundamental history (Linder et al., 2012).

However, little is known regarding whether ecoregion-like units vary when comparing functional groups. In plants, most ecoregional classifications thus far have been based on woody taxa alone (e.g., Olson et al., 2001). This is partly due to woody taxa playing a prominent role in determining vegetation structure in general and partly due to more data being available for woody rather than herbaceous taxa. Herbs are largely omitted in current definitions of vegetation types, even in areas where complete floras are available (e.g., UK system, US).

Woody and herbaceous taxa could be expected to form different ecoregions due to fundamental differences in their life histories. Life history includes variables such as fecundity throughout the life cycle and generation time the average age of reproducing mothers (Stearns, 1992). The life history for most 23 plant species is unknown, but some aspects of life history have been estimated with proxies. One well-known proxy for life history is growth form. Previous studies have examined the relationship between rates of molecular evolution and growth form (Laroche et al., 1997; Kay et al., 2006; Bromham et al., 2015). They suggest that heterogeneity in molecular substitution rates may be attributed to generation time (Smith and Beaulieu, 2009; Smith and Donoghue, 2008). Trees have been found to exhibit a lower rate of substitution compared to herbs (Smith and Donoghue, 2008; Beaulieu et al., 2012), presumably due to longer generation time. In this categorization, herbaceous groups such as palms and columnar cacti are considered woody due to their longer generation time (Beaulieu et al., 2012; Smith and Donoghue, 2008; Smith and Beaulieu, 2009).

Molecular evolution rates have been linked to niche evolution. Niche evolution is important in regards to ecoregions because it could describe how easily lineages evolve to experience new environments or ecoregions. Smith and Beaulieu (2009) used a time-based phylogenetic analysis to infer that phenotypic evolution is influenced by generation time and thus climatic niches occur differently for unique growth forms. They found that climatic niches tended to be more conserved over evolutionary time for woody species due to longer generation time and slower mutation rates. Qian and Ricklefs (2004) looked at geographical and climatic distributions of Eastern Asian and Eastern North American disjunct genera, aiming to understand climatic niche evolution. They found that herbaceous genera tended to show stronger niche conservatism compared to woody species. However, this study did not take time of disjunction into consideration. While these studies provide useful, albeit contradicting, results, the definitions of woody and herbaceous 24 species was rather straight forward with no confusion of subshrubs, epiphytic lianas, hemi-epiphytes, parasitic species or long generational species that may assume an herbaceous ecosystem function (i.e. many Cactaceae).

1.6 Study aims and hypotheses This study focuses on testing whether ecoregions, defined as categorical biogeographic divisions characterizing spatial variation in biotic composition at broad geographic scales, differ between herbaceous and woody growth forms. We use both taxonomic and phylogenetic β-diversity metrics for categorical wombling, a boundary detection method designed for depicting zones of rapid change across space, employing Nicaragua as a study system. The following two hypotheses were specifically tested.

(H1) Herbaceous species have faster rates of climatic niche evolution and thus would have weaker ecoregion limits based on results from Smith and Beaulieu (2009);

(H2) Herbaceous species have slower rates of climatic niche evolution and thus have stronger ecoregion limits based on the findings of Qian and Ricklefs (2004).

2 Methods

2.1 Flora of Nicaragua Nicaragua was chosen as the study area because it has one of the best studied tropical floras that is linked to a well curated, digitally available occurrence database. Nicaragua is a small country (130,000 km2) situated in the middle of Mesoamerica. It is phytogeographically intriguing because North American species reach their southern limits in its mountains and the southeastern part is the northern limit of Amazonian species. The north-central part of the country is mountainous, whereas the rest of the country is generally lowlands interspersed with volcanoes (Table 3). The country spans large environmental gradients relative to its small size, and includes all major tropical vegetation types, such as tropical lowland moist forest, seasonally dry tropical forest, savannahs and mountain cloud forest (Table 4). Southeastern corner experiences high annual rainfall with low seasonlaity, while northwestern areas receive little rain with high seasonality (Figure 2). Much of the rainfall pattern is related to the mountain ranges, which split the country into more continuously humid eastern side and seasonally dry western side (Figures 2-3).

Table 3. Description of the three primary zones in Nicaragua.

Zones Description Annual rainfall North-Central Central area around 600 m, dominated 600 mm – 2,400 mm by mountains reaching 2,000 m Pacific Pacific lowlands and volcanoes 900 mm - 1,800 mm Atlantic Entire Atlantic side with some 1,700 mm – 6,000 mm; isolated 700 m peaks strong east-west gradient

The Flora of Nicaragua was published in 2001 with 5,796 vascular plant species and that number has increased c. 10% since its publication (W.D. Stevens, MBG, pers. comm.). It was Nicaragua’s 26 first modern flora and first complete flora of a Latin American country published in Spanish. The flora is available online linked to a large specimen database and is continuously updated (www.tropicos.com/Project/FN). The flora now includes over 100,000 georeferenced, taxonomically verified, well-curated records of 5,982 species in 249 families, which account for 63% of species and 55% of families of the world’s vascular plants (Christenhusz and Byng, 2016).

Figure 2. Two important environmental factors in Nicaragua. Map (A) shows the annual rainfall gradient with wetter areas shown in blue and drier areas in brown. Map (B) shows the precipitation seasonality with areas of high seasonality in red and low seasonality in green. Layers derived from BioCLIM variables BIO12 (A) and BIO15 (B) (Hijmans et al., 2005). 27

Table 4. Vegetation types of Nicaragua recognized in Flora of Nicaragua (Stevens et al., 2001).

Vegetation Location Soil & rainfall traits Elevation Description Hyper-wet Southeast corner High rainfall (4,000-6000 Mostly Very diverse, woody and herbaceous lianas & rainforest mm), no dry months <100 m epiphytes abundant; Rubiaceae & Melastomataceae in understory Wet forest Across the 2,000-4,000 mm, 2-4 months 0-800m Mainly evergreen, lots of lianas and epiphytes; country dry high diversity Cloud forest NCZ Covered with cloud layer 600-800m Rich diversity of epiphytes; Quercus & Lauraceae Elfin forest Exposed peaks & Strong, constant wind, high >800m Scrubland and bryophytes, Bromeliaceae and highest mountain humidity, unstable slopes and Orchidaceae common among bryophytes; same summits frequent storms species as cloud forest but smaller and more densely branched; Clusia Pine savannah Scattered 2.5-3.5 mm, extremely poor Cyperaceae in poorer soils; Poaceae in better patches along soil (sand, gravel); frequent drained soils; transition zone dominated by Atlantic coast fires Rubiaceae & Melastomataceae; Pinus caribaea var. hondurensis Pine & pine- Highlands of Acidic soils (granite, 650m Herbaceous vegetation rich and diverse, oak forest NCZ; Upper & schist), 1,000-2,500mm; fire dominated by Poaceae, Cyperaceae & Fabaceae; middle slopes common Pinus maximinoi, P. oocarpa, P. tecunumannii, Quercus Dry forest Pacific Zone; 20-25m canopy, vines rare, low diversity; <1% persists epiphytes common, low diversity Calabash Pacific coast & Shallow rocky soils, regular Crescentia alata (diagnostic species); Poaceae savannah drier low-lying burning (Aristida ternipes, Bouteloua alamosana, areas of NCZ Oplismenus burmannii var. nudicaulis) Zonzocuitales Lagoons near Clay, heavy, black, highly Low diversity of woody plants (small and lakes; Pacific mineralized, poorly drained restricted), annuals more diverse, weed-like coast & central soils; flooded (rainy appearance (restricted) part of country season), deep cracks (dry season) Forest Along waterways Frequent flooding during Ficus and Inga specifically adapted to habitat galleries rainy season, saturated soils Swamp forests Coastal lowlands Frequent flooding; saturated Sea level Most dominated by herbaceous plants (Cyperaceae & around great soils & Poaceae) lakes Mangroves Both coasts Periodic submersion of Sea level Low diversity, species restricted to habitat; saltwater Rhizophora mangle, Laguncularia racemosa, Conocarpus erectus, Avicennia nitida

Beaches Both coasts Continuous disturbance & Sea level Restricted species salinity effects 28

2.2 Species occurrence data Data was downloaded from the extensively studied Nicaraguan vascular plant flora and the associated database of georeferenced and taxonomically verified specimens that is actively curated and updated online, available through TROPICOS (www.tropicos.org/Project/FN; Stevens et al., 2001). The digitally available data included 152,328 records, of which 92% were determined to species and 99% had coordinate data. All 5,982 unique species names in the Flora of Nicaragua, including infraspecific taxa e.g. subspecies, varieties and forms, were matched to all 152,328 specimen records for a total of 130,110 specimen records and 5,844 unique species names. Fifty-six records from cultivated and introduced species were removed from the dataset resulting in 130,080 specimen records and 5,819 unique species names. Just fewer than 300 records were discarded due to falling ≥ 2 km from the coastline of Nicaragua; those records falling within 2 km were assigned new coordinates corresponding to the center of the nearest grid cell on land. Of the remaining 129,797 specimen records, the number of unique grid cells for each species was calculated.

In order to understand changes in β-diversity across space, the locality data was used to estimate species diversity and composition across Nicaragua with the implementation of Species Distribution Modelling (SDM) on individual species. It is known that low sample size has a negative effect on model performance; therefore, a conservative threshold of ≥37 geographically unique samples at 1 x 1 km resolution was required for species in analysis (Wisz et al., 2008). There was further elimination of three “unmodellable” species whose distributions are driven by ecological and environmental factors that were not used in the predictor dataset (freshwater and mangrove species) and 11 29

commonly cultivated species were rejected because of difficulty in separating cultivated from native/naturalized records (Tables 5-6). The final dataset for SDM included 911 species in 141 vascular plant families with 75,233 specimen records.

Table 5. List of three species excluded from analysis based on inability to model due to distributions being driven by ecological and environmental factors that were not used in the predictor dataset, which focused primarily on climatic variables.

Species Reason excluded Ipomoea pes-caprae (L.) R. Br. occurring in mangroves, beaches and lake shores Marathrum foeniculaceum Bonpl. restricted to freshwater habitats Rhizophora mangle L. restricted to mangrove habitats

Table 6. List of 11 commonly cultivated species excluded from the analysis due to difficulty in separating cultivated records from native and/or naturalized.

Commonly cultivated species Allamanda cathartica L. Antigonon leptopus Hook. & Arn. Bixa orellana L. Caesalpinia pulcherrima (L.) Sw. Chrysobalanus icaco L. Marathrum foeniculaceum Bonpl. Persea americana Mill. Plumeria rubra L. Psidium guajava L. Solanum seaforthianum Andrews Sphagneticola trilobata (L.) Pruski

2.3 Species Distribution Modeling MaxEnt v.3.3.3k (Phillips et al., 2006; Phillips and Dudik, 2008) was used to produce SDMs, in which potential species distributions are predicted from presence-only data with environmental variables, based on the maximum entropy principle (Phillips et al., 2004). Species presences were pruned to one occurrence per grid cell. Fourteen climatic predictors were selected because of their expected influence on plant distributions across the area, including 12 commonly used bioclimatic variables: mean annual temperature, diurnal 30 temperature range, isothermality, temperature seasonality, maximum temperature of the warmest month, mean annual precipitation, precipitation of the driest month, precipitation seasonality, and precipitation of the wettest, driest, warmest, and coldest quarters. The other variables were the climatic water balance (precipitation minus potential evapotranspiration) of the driest month derived using a modified Hargreaves equation (Droogers and Allen, 2002) and the longest number of consecutive months in a 12-month period with total precipitation <1 mm (Table 7). Variables were derived from the monthly-interpolated temperature variables in WORLDCLIM v.1.3 Rel. 3 (Hijmans et al., 2005) and the monthly radar detected precipitation variables in TRMM (www.ambiotek.com/1kmrainfall). Highly correlated variables (R ≥ 0.9) were removed because uncorrelated variables tend to overestimate distributions but removing too many variables could lead to species without predictors and ultimately a poor model (Kramer-Schadt et al., 2013).

Table 7. List of all variables available for variable selection. BioCLIM variables BIO1-19 were derived from WORLDCLIM temperature data v.1.3 Rel. 3 (Hijmans et al., 2005) and TRMM radar-detected rainfall data (www.ambiotek.com/1kmrainfall). Climatic water balance layers (CWB1-19) were calculated using a modified Hargreaves equation (Droogers and Allen, 2002) based on the monthly temperature variables of WORLDCLIM and TRMM rainfall data. RAIN0 was calculated from the TRMM rainfall data for this study (A.B. Smith, Missouri Botanical Garden, unpubl. data). Variables highlighted in grey were selected as predictor variables for species distribution modeling.

Name Variable description Unit BIO1 Annual mean temperature °C * 10 BIO2 Mean diurnal range (mean of monthly (max temp - min temp)) °C * 10 BIO3 Isothermality (BIO2/BIO7) °C * 1000 BIO4 Temperature seasonality SD * 100 BIO5 Max temperature of warmest month °C * 10 BIO6 Min temperature of coldest month °C * 10 BIO7 Temperature annual range (BIO5-BIO6) °C * 10 BIO8 Mean temperature of wettest quarter °C * 10 BIO9 Mean temperature of driest quarter °C * 10 BIO10 Mean temperature of warmest quarter °C * 10 BIO11 Mean temperature of coldest quarter °C * 10 BIO12 Annual mean precipitation mm BIO13 Precipitation of wettest month mm BIO14 Precipitation of driest month mm BIO15 Precipitation seasonality (coefficient of variation) BIO16 Precipitation of wettest quarter mm BIO17 Precipitation of driest quarter mm BIO18 Precipitation of warmest quarter mm BIO19 Precipitation of coldest quarter mm CWB13 CWB of the wettest month mm CWB14 CWB of the driest month mm CWB15 CWB seasonality (coefficient of variation) CWB16 CWB of the wettest quarter mm CWB17 CWB of the driest quarter mm CWB18 CWB of warmest quarter mm CWB19 CWB of coldest quarter mm Longest number of consecutive months in a 12-month period Number of RAIN0 with total precipitation >1 mm months

In order to produce robust SDMs, controlling for bias in collection efforts is important especially when study aims include predicting species distributions in under-collected areas (Kramer-Schadt et al., 2013). Such approaches take into account how much any given grid cell has been visited and the probability of finding the study species in question. Within Nicaragua, collecting bias is evident, where most collections have been made following the road network (Figures 4-5). Two 32 different bias correcting methods were used in this study. Bias set one, henceforth referred to as the target background bias set, comprised of 10,000 sites drawn proportionate to the number of specimens of all species (including aquatic and cultivated species) in each cell (Phillips et al., 2009). Bias set two, henceforth referred to as kernel density bias set, was generated using a kernel density estimator with an Epanechnikov kernel, as per Elith et al. (2010). The density estimator was prepared with all occurrences of vascular plants for the entire Nicaragua, after which background 10,000 sites were drawn proportionate to the density estimator.

Figure 3. Map with all 139,954 geo-referenced specimen records from Tropicos (in yellow) for all vascular plant species, overlaid with 75,925 specimen records (in blue) for the 786 plant species included in this study. Elevation, water boundaries and roads are shown. Elevation TIF file was downloaded from the WORLDCLIM database (www.worldclim.org/tiles.php?Zone=23). Water boundaries and road shapefiles were downloaded from DIVA-GIS (www.diva- gis.org/gdata). 33

Figure 4. Maps of collection density in Nicaragua at 1 x 1 km resolution (top row) and 5 x 5 km resolution (bottom row). Maps in left column contain all species in the dataset (786), center column maps represent herbaceous species only, and maps in the right column represent woody species. Both models (target and kernel density bias file models) for each species were modelled five times using 80% of the presences for training and the remainder for testing on both background sets (i.e., 5k cross-validation approach). A modeling threshold of the 0.1-quantile value of training presences was implemented on the models because of a safe assumption of little georeferencing and/or taxonomic identification error.

Model performance was assessed using the Continuous Boyce Index (CBI; Boyce et al., 2002; Hirzel et al., 2006) designed for presence-only datasets. The Boyce index assesses how much the model differs from random expectation across the entire study 34 area with predicted-to-expected ratio curves, which allow understanding of model quality, including robustness (variance), habitat suitability (curve shape) and deviation from randomness (maximum value) (Hirzel et al., 2006). CBI uses a moving window rather than fixed classes, which creates a smooth P/E curve (Hirzel et al., 2006). CBI values range from -1 to 1; when values >0, model performance is better than random and indicates the disposition of test presences to “sample” cells in proportion to their appropriateness and predominance across the landscape (Li and Guo, 2013). CBI values <0 indicate model performs worse than random. CBI values fell below 0.5 in both models for 18 species, and these were discarded from further analysis (Table 8), resulting in a dataset of 891 species. For these species, models with the highest CBI value were chosen from the two background bias sets. Analyses used custom code with R packages “dismo” (Hijmans et al., 2014a, “raster” (Hijmans et al., 2014b) and “adehabitat” (Calenge, 2014).

Table 8. List of 18 species removed from further analysis due to poor model performance with Continuous Boyce Index values falling below 0.5.

Species removed due to poor model performance Blepharodon mucronatum (Schltdl.) Decne. Chamaedorea tepejilote Liebm. Cissus verticillata (L.) Nicolson & C.E. Jarvis Cordia collococca L. Cyclopeltis semicordata (Sw.) J. Sm. Cyperus laxus Lam. Desmodium triflorum (L.) DC. Hirtella americana L. Homalium racemosum Jacq. Hygrophila costata Nees & T. Nees Pityrogramma calomelanos (L.) Link Polygonum punctatum Elliott Serjania mexicana (L.) Willd. Solanum jamaicense Mill. Steinchisma laxum (Sw.) Zuloaga Stigmaphyllon ellipticum (Kunth) A. Juss. Tithonia rotundifolia (Mill.) S.F. Blake Ximenia americana L. 35

2.4 Flora phylogeny

2.4.1 Phylogenetic tree estimation Community phylogeny was estimated for the 891 modelled species of the Flora of Nicaragua using published sequences in GenBank (http://www.ncbi.nlm.nih.gov/genbank/) in order to estimate phylogenetic β-diversity values between grid cells (see details below under 2.6 Categorical wombling). Advanced search of GenBank was performed for all SDM target species. Regions in analysis included matK, atpB, rbcL and ndhF and cluster matching was done using BLAST cluster analysis as implemented in USEARCH (Edgard, 2010). Alignments were done in MAFFT (Katoh et al., 2005; Katoh and Toh, 2008) with trimming and manual adjustments to minimize missing data. Duplicate accessions for each species were removed and all regions were concatenated. Taxa that had missing sequence data were replaced with congenerics based on expert knowledge (e.g., Begonia, Psychotria, Solanum), or in the case that the taxon was the only representative of the genus/family in Nicaragua, a different species in the same genus/family was used according to availability without consulting expert knowledge or data sources. No sequence data was found for 105 taxa from the SDM models and thus these were removed from further analysis.

The final supermatrix contained 786 taxa and was analyzed in Raxml v. 8.2.6 (Stamatakis, 2006a) using the CIPRES portal (Miller et al., 2010) with 10 replicate searches for the best tree with GTRCAT and 1,000 bootstrap replicates (Stamatakis 2008, 2006b). Release of APGIV (2016) and recent molecular systematic studies (Xi et al., 2012) were the basis for constraining specific nodes where our dataset had poor resolution, including family relationships within Malpighiales and Commelinids, order level relationships between Fabales, 36

Rosales, Fagales and Cucurbitales, and the sister relationship between Monocots and Eudicots. The resulting phylogeny had, because of the constraining, well-supported nodes for all major clades within known Angiosperms based on APGIV (2016). The final tree with the dataset of 786 modelled species distributions was then used for all further analyses using categorical wombling (Appendix 1).

2.4.2 Time calibration The best maximum likelihood tree was time-calibrated using a penalized likelihood rate-smoothing algorithm (Sanderson, 2002) as implemented in treePL (Smith and O’Meara, 2012). A relaxed clock deals better with differences in metabolic rate, generation time, mutation rate and effective population size (Rutschmann, 2006) but may insufficiently capture the degree of molecular variation in empirical phylogenies leading to incorrect estimations of absolute rates and divergence times (Magallón et al., 2015). Penalized likelihood uses a semi- parametric approach to incorporate rate heterogeneity and allows for different rates on different branches but has a smoothing parameter, set using cross-validation, that affects how much large-scale rate variation is penalized between sister lineages, thus achieving the power of parametric methods and robustness of non-parametric techniques (Rutschmann, 2006). The implementation of penalized likelihood rate smoothing in treePL now allows effective and fast handling of large datasets. The only concern is the inability to incorporate branch length errors or parameters of substitution model in penalized likelihood analysis (Rutschmann, 2006).

Secondary age estimates derived from the most recent data and fossil-calibrated molecular phylogenetic studies of angiosperms, ferns and land plants were used to calibrate the tree (Magallón 37 et al., 2013, 2015; Rothfels et al., 2015) and due to limited sampling only the most appropriate crown and stem ages were set as secondary calibration points (Table 9). Maximum and minimum boundaries were derived from the available 95% confidence intervals or standard deviations of the original analyses (Table 9). Full cross-validation of eight smoothing parameters between 0.000001 and 10 led to the selection of 0.000001 as the rate smoothing parameter. The final tree is shown in Figure 5.

Figure 5. Phylogeny of 786 taxa of the Flora of Nicaragua studied here, with taxa colored to represent herbaceous (green) and woody (brown) growth form. Major orders represented in the dataset are labelled. 38

Table 9. Secondary calibrations used to constrain ages of major nodes across vascular plants. Clades for which minimum and maximum ages were derived from uncorrelated relaxed lognormal clock model are highlighted in grey; these ages represent the 95% posterior density intervals. All other ages represent penalized likelihood bootstrap ranges.

Node Min age Max age Taxon 1 Taxon 2 Reference Vascular plants crown 416 434 Solanum torvum Lycopodiella cernua Magallón et al. (2013) Schizaeoid ferns stem 205 235 Anemia phyllitidis Adiantum concinnum Rothfels et al.(2015) Tree ferns stem 179 238 Cyathea multiflora Adiantum concinnum Rothfels et al.(2015) Monilophytes stem 401 422 Solanum torvum Adiantum concinnum Magallón et al. (2013) Mesangiospermae crown 135.12 136.38 Virola sebifera Solanum torvum Magallón et al. (2015) Magnoliidae stem 134.37 135.75 Virola sebifera Monstera adansonii Magallón et al. (2015) Magnoliidae crown 132.77 134.51 Virola sebifera Piper jacquemontianum Magallón et al. (2015) Magnoliales stem 129.64 132.85 Guatteria amplifolia Siparuna thecaphora Magallón et al. (2015) Monocotyledoneae crown 131.9 133.82 Syngonium angustatum Cyperus ligularis Magallón et al. (2015) Liliales stem 118.83 122.79 Smilax spinosa Hypoxis decumbens Magallón et al. (2015) Asparagales stem 117.59 121.78 Prescottia stachyodes Geonoma deversa Magallón et al. (2015) Asparagales crown 112.58 118.15 Prescottia stachyodes Hypoxis decumbens Magallón et al. (2015) Arecales stem 108.94 115.94 Geonoma deversa Heliconia latispatha Magallón et al. (2015) Commelinales stem 95.33 104.04 Commelina erecta Heliconia latispatha Magallón et al. (2015) Poales stem 112.29 117.45 Tillandsia bulbosa Calathea lutea Magallón et al. (2015) Poales crown 101.54 107.01 Tillandsia bulbosa Olyra latifolia Magallón et al. (2015) Eudicotyledoneae crown 130.32 132.01 Bocconia frutescens Lippia myriocephala Magallón et al. (2015) Pentapetalae crown 122.83 124.81 Davilla nitida Lippia myriocephala Magallón et al. (2015) Santalales stem 121.56 124.17 Coccoloba acuminata Passovia pyrifolia Magallón et al. (2015) Santalales crown 98.77 108.72 Schoepfia schreberi Passovia pyrifolia Magallón et al. (2015) Caryophyllales crown 102.5 107.13 Coccoloba acuminata Pisonia aculeata Magallón et al. (2015) Asteridae crown 115.75 118.96 Gronovia scandens Lippia myriocephala Magallón et al. (2015) Ericales stem 114.18 117.73 Ardisia revoluta Viburnum hartwegii Magallón et al. (2015) Garryidae stem 111.41 115 Cordia alliodora Lobelia laxiflora Magallón et al. (2015) Garryidae crown 107.74 111.76 Cordia alliodora Solanum torvum Magallón et al. (2015) Solanales crown 90.44 97.05 Solanum torvum Hydrolea spinosa Magallón et al. (2015) Gentianales crown 77.98 85.35 Psychotria grandis Asclepias woodsoniana Magallón et al. (2015) Boraginales stem 95.94 101.75 Cordia alliodora Ruellia paniculata Magallón et al. (2015) Lamiales crown 84.28 91.83 Columnea nicaraguensis Ruellia paniculata Magallón et al. (2015) Asterales stem 100.48 106.98 Baltimora recta Viburnum hartwegii Magallón et al. (2015) Apiales crown 92.46 100.86 Oreopanax capitatus Eryngium foetidum Magallón et al. (2015) Superrosidae crown 122.16 124.22 Vitis tiliifolia Passiflora bicornis Magallón et al. (2015) Rosidae crown 117.57 120.13 Clidemia dentata Senna uniflora Magallón et al. (2015) Malvidae crown 116.89 119.91 Sida cuspidata Picramnia antidesma Magallón et al. (2015) Myrtales crown 93.69 99.54 Miconia lacera Combretum fruticosum Magallón et al. (2015) Sapindales stem 109.64 113.91 Paullinia fuscescens Carica papaya Magallón et al. (2015) Fabidae crown 116.07 119.06 Kallstroemia maxima Polygala paniculata Magallón et al. (2015) Malpighiales stem 111.67 114.84 Cnestidium rufescens Croton niveus Magallón et al. (2015)

2.5 Analysis of representativeness

2.5.1 Taxonomic The taxonomic representativeness of the final dataset was compared with the distribution of families and orders of the complete taxa list in the Flora of Nicaragua (Stevens et al., 2001; www.tropicos.org/Project/FN). The orders were found to be comparatively similar to the entire known vascular flora (Stevens et al., 2001), except for less specious Asparagales and more specious Caryophyllales (Table 10). Dataset families were found to follow complementary patterns when compared to the flora (Stevens et al., 2001), with the exceptions of under- representation of Orchidaceae and Poaceae (Table 11).

Table 10. Top ten orders represented in dataset and Flora of Nicaragua. Orders not shared in both sample sets are bolded.

Dataset Flora of Nicaragua 786 species, 42 orders 5983 species, 69 orders Top Ten Number of species Top Ten Number of species Fabales 80 Asparagales 639 Gentianales 74 Poales 620 Malpighiales 66 Fabales 523 Lamiales 58 Polypodiales 417 Asterales 56 Malpighiales 386 Poales 47 Gentianales 375 Myrtales 37 Lamiales 349 Caryophyllales 33 Myrtales 270 Malvales 31 Asterales 267 Polypodiales 31 Solanales 196

Table 11. Top ten families represented in dataset and Flora of Nicaragua. Families not shared in both sample sets are bolded.

Dataset Flora of Nicaragua 786 species, 138 families 5983 species, 249 families Top Ten Number of species Top Ten Number of species Fabaceae 79 Orchidaceae 610 Asteraceae 54 Fabaceae 495 Rubiaceae 50 Poaceae 302 Malvaceae 28 Asteraceae 254 Euphorbiaceae 27 Rubiaceae 241 Melastomataceae 24 Cyperaceae 197 Apocynaceae 20 Melastomataceae 173 Orchidaceae 19 Malvaceae 151 Poaceae 19 Piperaceae 128 Solanaceae 19 Euphorbiaceae 119 40

Families per order were calculated for the dataset and compared to the Flora of Nicaragua with strong representation in most orders, except orders basal to Rosids (Ericales and Cornales) and some Monocot orders (Poales, Alismatales, and Liliales) (Table 12).

Table 12. Number of families per order comparison between dataset and Flora of Nicaragua. Percentages <50% are highlighted in grey. Ferns are shown with an asterisk and hornworts are shown with double asterisks.

Order Dataset Flora % Fabales 2 3 67% Rosales 5 6 83% Cucurbitales 2 2 100% Fagales 2 5 40% Malpighiales 14 22 64% Zygophyllales 1 2 50% Malvales 4 6 67% Brassicales 4 7 57% Sapindales 6 6 100% Picramniales 1 1 100% Crossosomatales 1 1 100% Myrtales 6 6 100% Vitales 1 1 100% Gentianales 4 4 100% Lamiales 11 16 69% Solanales 3 3 100% Boraginales 1 1 100% Asterales 2 3 67% Apiales 2 2 100% Dipsacales 2 2 100% Ericales 7 15 47% Cornales 1 2 50% Caryophyllales 10 16 63% Santalales 3 7 43% Dilleniales 1 1 100% Ranunculales 3 4 75% Poales 3 8 38% Zingiberales 5 5 100% Commelinales 3 3 100% Arecales 1 1 100% Asparagales 3 5 60% Liliales 1 3 33% Alismatales 1 6 17% Piperales 2 2 100% Laurales 4 4 100% Magnoliales 2 3 67% Cyatheales* 1 3 33% Polypodiales* 7 18 39% Schizaeales* 2 3 67% Selaginellales** 1 1 100% Lycopodiales** 1 1 100% 41

2.5.2 Growth form

Categorical The representation of growth forms in the final dataset of 786 was analyzed against 1,000 randomly sampled species from the flora of Nicaragua. The growth form for these species was assigned into nine categories that we then consequently lumped into woody or herbaceous. Shrubs, trees and lianas were considered as woody, while herbs, vines, epiphytes, hemi- ephiphytes, parasites, saprophytes and aquatics were considered herbaceous. The growth form representation of the final dataset of 786 samples was then analyzed using Pearson’s chi-squared test with goodness of fit (Tables 13-14, Figure 6). All p-values were <0.0001, strongly indicating a significant difference in the representation of growth forms between the final dataset and the entire Flora of Nicaragua (Tables 13-14). Herbs, trees, vines and epiphytes were slightly under-represented, while shrubs and lianas were slightly over-represented (Figure 6). Most skewness of the dataset was caused, however, by the under- representation of herbaceous taxa in general, which dominate the Flora of Nicaragua (Figure 6). The relatively even number of taxa in both woody and herbaceous categories in our sampled dataset does, however, indicate that the representation of the two growth forms is adequate for comparing differences in ecoregions between the two.

Table 13. Chi-squared calculations for growth forms in the dataset and a random sample of 1,000 species from the Flora of Nicaragua.

Dataset Percent Sample Percent Sum Calc Residual Chi-squared Growth form (observed) % (expected) % (Obs) (Exp) (Obs-Exp) value Shrub 227 28.9% 201 20.1% 428 188 38.642 7.92732 Tree 141 17.9% 164 16.4% 305 134 6.773 0.34173 Liana 30 3.8% 11 1.1% 41 18 11.956 7.92265 Herb 268 34.1% 331 33.1% 599 264 4.386 0.07299 Vine 52 6.6% 104 10.4% 156 69 -16.654 4.03990 Epiphyte 58 7.4% 150 15.0% 208 92 -33.539 12.28814 Hemi-epiphyte 5 0.6% 22 2.2% 27 12 -6.882 3.98637 Aquatic 0 0.00% 14 1.4% 14 6 -6.161 6.16125 Parasite & Saprophyte 5 0.6% 3 0.3% 8 4 1.479 0.62154 Total 786 100.0% 1000 100.0% 1786 786 43.36190

Degrees of freedom 8 p <0.0001

Table 14. Chi-square calculations for summed woody and herbaceous growth forms. See Methods for details about scoring.

Summed growth form Dataset % Sample % Sum Calc Residual Chi-squared Obs Exp value Woody 398 50.6% 376 37.6% 774 341 57 9.66268 Herbaceous 388 49.4% 624 62.4% 1012 445 -57 7.39023 Total 786 100.0% 1000 100.0% 1786 17.05291

Degrees of freedom 1 p <0.0001

Figure 6. Graphs of species per growth form for dataset and 1000 species randomly sampled from Flora of Nicaragua. Graph A represents Table 13 with dotted line separating woody species (left) from herbaceous species (right). Graph B represents Table 14 and the growth forms used for analysis. 43

Species diversity Species diversity gradient maps were created in R for all species in the dataset and the two growth form categories. In addition, diversity differences between the two growth forms were analyzed by creating maps of (a) herbaceous species diversity was subtracted from all species diversity, (b) woody species diversity subtracted from all species diversity, and (c) herbaceous and woody species diversities subtracted from each other. The map results show higher species diversity along the western, more humid and aseasonal side of Nicaragua with more herbaceous species found in the northern mountaneous areas while the lowland flora is dominated by woody species (Figure 7).

Figure 7. Gradient maps of species diversity for all species (A), herbaceous species (B) and woody species (C). Maps D-F show differences in species diversity, including all species minus herbaceous species (D), all species minus woody species (E) and herbaceous minus woody species (F). 44

Range size Range size calculations were performed in R with comparison between growth forms using modelled data. Area of occupancy (AOO) and extent of occurrence (EOO) showed similar graphical patterns in both growth forms, indicating that neither growth form was restricted to short range species (Figure 8). It is known that range size is restricted to the shape of the land mass. Of particular interest in this dataset, the bimodal pattern of the AOO would indicate two distinct “land masses” or more likely, one land mass acting as two.

Figure 8. Range size calculations. Graphs for range sizes of herbaceous species (green) and woody species (brown) (bottom row). Area of occupancy (AOO) is represented on the left and extent of occurrence (EOO) is represented on the right.

Phylogenetic distance The patristic distance method was performed on the dataset and growth forms were compared to examine the phylogenetic robustness of the dataset using PATRISTICv1.0 (Fourment and Gibbs, 2006). Patristic distance is the summed branch length 45 between two terminal taxa. The mean patristic distances, i.e. total branch lengths measures from the time-calibrated tree in millions of years, were found to be statistically similar between herbaceous and woody taxa. Although, herbaceous have slightly longer branches (more evolutionary change) than woody, which is understandable due to inclusion of basal vascular plants (hornworts and ferns) (Figure 9)

Figure 9. Patristic distance histogram of taxa by growth form. One very important aspect in the dataset is the absent Gymnosperm lineage. None of the gymnosperm species made the 37 geographical occurrences limit and thus were removed from analysis. This phenomenon could be interesting, as key species for identifying certain vegetation types (Pinaceae in pine savannah and pine-oak forests) were not analyzed. However, it was not the scope of this project to look at specific ecoregions but rather vascular plants in Nicaragua as a whole. 46

2.6 Categorical wombling

2.6.1 Measuring β-diversity Whittaker (1972) was the first to define β-diversity as "the extent of species replacement or biotic change along environmental gradients". β-diversity measures species turnover between two sites in connection with species gain or loss. The concept of change in species composition is clear and intuitive, but β-diversity contains two contradictory phenomena: nestedness and turnover. Nestedness arises when the biota of a locality with a reduced number of species is a subset of a biota with a larger number of species. This means that dissimilarity between two sites is related to the difference in species richness and takes place even in the absence of a true species turnover. Spatial turnover signifies replacement of some species by others. Various β-diversity indices are sensitive, with a multitude of degrees, to the variation in species richness, resulting in values linked to both nestedness and turnover and differ in statistical properties (Koleff et al., 2003). To understand biogeographical regionalizations, richness- independent turnover is more explanatory and therefore metrics that are least affected by richness variation should be used (Kreft and Jetz, 2010).

Categorical wombling was applied to Nicaragua using estimated species diversity and community composition data derived from 786 thresholded SDMs. To discover boundaries, we incorporated both taxonomic and phylogenetic pairwise species turnover metrics to understand ecoregion existence and potential similarities between growth forms (a) as relevant biological units in ecological time scale and (b) as evolutionarily valid meta-communities that describe correlation and resemblance of 47 communities through evolutionary time (Pennington et al., 2009). β-diversity between all adjacent sites was calculated using

Simpson index (βsim; Figure 10A; Simpson, 1943, 1960) because this measure is not sensitive to variation in species richness across communities. It computes a pure turnover component of β- diversity (i.e., replacement of species) which was of interest to the study, without nestedness component as in the commonly used Sørensen index (βsor; Lennon et al., 2001; Mouillout et al., 2013). Total dissimilarity, such as the Sørensen index, can be additively partitioned into replacement (βsim) and nestedness- resultant elements (Baselga, 2010; Baselga, 2012) as well as separate constituents for abundance-based dissimilarity (Baselga, 2012; Legendre, 2014), functional dissimilarity (Villeger et al., 2013) and phylogenetic dissimilarity (Leprieur et al., 2012). Phylogenetic β-diversity was determined with the phylogenetic turnover metric of β-diversity equivalent to

Simpson index (pβsim; Figure 10B) which uses branch lengths of a time-calibrated phylogeny instead of species composition (Lepriur, et al. 2012, Graham and Fine 2008). Phylogenetic β- diversity assesses the phylogenetic distance amidst communities (Leprieur et al., 2012; Graham and Fine, 2008). The use of phylogenetic β-diversity takes into account the different scales that ecological and evolutionary processes might inflict on community composition (Chave et al., 2007; Graham and Fine, 2008). Branch lengths were used, not just branch counts, in order to ensure correct age of clades were incorporated (Kreft and Jetz, 2010). Both βsim and pβsim were applied to calculate β- diversity between all neighboring grid cells. β-diversity values were then ranked through random resolution of ties to identify CBEs. Superfluity was computed for both β-diversity metrics and results were compared to two null models (categorical and geographic; see below) to determine significant results. All 48 categorical wombling analyses were completed in the R statistical environment using packages “sp” (Pebesma and Bivand, 2005), “raster” (Hijmans et al., 2014b), “igraph” (Csardi and Nepusz, 2006) and “ape” (Paradis et al., 2004).

Figure 10. Simpson Index used for βsim (A) and pβsim (B). In equation A, a represents shared species, b represents unique species to one community and c represents unique species in the other community. In equation B, PDTot represents the sum of branch lengths for communities j and k, PDk represents the sum of branch lengths for community k and PDj represents the sum of branch lengths for community j. Two spatial scales were considered: 30-arc second (c. 1 km) and 0.05-degree (c. 5 km). Scale size is important to contemplate as a resolution of less than 2 degrees can over-estimate the area of occupancy for an individual species and potentially mischaracterize spatial patterns (Hurlbert and Jetz, 2007). Also finer resolution often exposes fluctuations hidden by coarse resolution but sensitivity increases with refinement (Whittaker et al., 2005). SDMs were created with a 30-arc second scale and all categorical wombling analyses were run using 0.05-degree scale based on time constraints and study area size. Unequal area size by grid cell has no effect due to minimal change in latitude.

2.6.2 Categorical wombling method The statistical technique of wombling that has been developed for identifying regions of rapid change across a geographical or Euclidean space based on candidate boundary elements and the superfluity concept (Womble, 1951; Oden et al., 1993) was employed in this study. The method was originally developed for continuous variables of gene frequencies and morphological measurements by Womble (1951) as a “systemic function” defined over geographic space, when mapped reveals zones of rapid change. Oden et al. (1993) expanded the method for inclusion of categorical variables, referred to as categorical wombling, mentioning specifically distribution of plant species in vegetation maps to find ecotones. The categorical wombling method exhibits whether links between geographically adjacent sites are linked to significantly more change in underlying categorical variables than may occur by chance (Fortin & Drapeau 1995). The categorical wombling statistic is computed at the ith Delaunay link and a proportion of the number of category mismatches at that Delaunay link is determined across all surfaces, calculated separately for each n Delaunay link (Oden et al., 1993). Only dissimilarities between adjacent locales are considered for quantitative or qualitative data, such as taxonomic or genetic distance (Barbujani et al., 1990) or a simplistic mismatch measure (Oden et al., 1993). These values are then ranked with a proportion of the highest dissimilarity values picked based on an arbitrary threshold (e.g. top 10%) and designated as candidate boundary elements (CBE; Fortin and Drapeau, 1995). There are several statistics to understand whether links are correlated with rapid changes such that (a) links are significantly arranged into geographical boundaries and (b) whether these boundaries tend to encircle homogenous regions (Oden et al., 1993). The Delaunay link corresponding to 50 the highest CBE value is replaced with a dual Thiessen edge and this process is continued by magnitude of the CBE values. Eventually, once enough Delaunay links are replaced, the Delaunay graph severs into two disconnected subgraphs with internally connecting Thiessen edges (Oden et al., 1993). As this process continues, Delaunay tessellation will break into three and more internally connected Delaunay subgraphs separated by more complex Thiessen boundaries (Oden et al., 1993).

A drawback of selecting boundary elements with arbitrary threshold is this choice may not be suitable for all circumstances (Fortin and Drapeau, 1995). A boundary statistic, such as superfluity, is utilized to determine inefficiency of connected CBEs (i.e. ability to divide the study area into regions) and assists in identifying significant regions. Two subgraphs of connected Delaunay tessellation could be completely separated from each other by an efficient Thiessen edge boundary or a meandering one (Oden et al., 1993). Unnecessary CBEs are boundaries whose removal would not change the number of regions, while necessary CBEs are described as boundaries whose removal would decrease the number of regions. Superfluity is calculated by dividing the number of unnecessary CBEs by the necessary CBEs, i.e. necessary rates of change that separate a study region into units. Low values express the presence of only a few cohesive boundaries, whereas high values indicate the existence of several boundaries scattered throughout the study region where most CBEs do not contribute to dividing the zone into distinct units (Fortin and Drapeau, 1995). Superfluity value significance is only indirectly related to the significance of the boundary (Oden et al., 1993). Rather, superfluity reacts preferentially to regions not merely to boundaries as one cannot have regions without boundaries but boundaries can exist without 51 regions. Null models can be adapted for the comparison of whether the observed number of regions and the associated superfluity values are significantly different from expected by chance.

Figure 11. Predicted outcomes for each hypothesis for each percentile test using either taxonomic or phylogenetic β-diversity metric. H1: herbaceous taxa have weaker ecoregion limits than woody taxa; H2: herbaceous taxa have stronger ecoregion limits than woody taxa. CBE = Candidate Boundary Element.

2.6.3 Null models A null model is “a pattern-generating model that is based on randomization of ecological data or random sampling from a known or imagined distribution”, which mimics the outcome of a random 52 process model without specifying or estimating all parameters (Gotelli and Graves, 1996; Gotelli and Ulrich 2012). These kinds of models provide flexibility and specificity that cannot be obtained with conventional statistical methods (Gotelli, 2001). Simplicity of null models is strongly supported as imposing too many constraints reduces power and increases probability of a type II error (false negative) (Gotelli and Ulrich, 2012). A null model is constructed using real data that deliberately excludes the mechanism being tested and the metric choice arises naturally from the hypothesis (Gotelli, 2001; Gotelli and Ulrich, 2012). Null hypotheses do not show if entire communities are non-structured or random, but rather the community structure is random in relation to the mechanism being tested (Gotelli, 2001).

Geographical Null Models (GNM) Geographical null models were used to exclude the structure causing ecoregions while maintaining the observed range size distribution of the study species. For this, the range cohesion null model that controls for species diversity differences and spatial structures of species ranges were modified (Rahbek et al., 2007). Range sizes, in terms of Area of Occupancy (AOO) and Extent of Occurrence (EOO), were preserved in 10% windows during preservation range spatial cohesion. Centroids for null model ranges were randomly chosen within Nicaragua. The geographic centroid of the species occurrences was shifted to a randomly chosen point within the study area. A random angle was selected in which occurrences were rotated about the new centroid (Figure 12). This process was repeated until all occurrences fell onto land within the study area using “sp” 1.0-14 (Pebesma and Bivand, 2005) and “raster” 2.1-66 (Hijmans, 2014b) packages in R. Therefore, the geographical null model preserved all aspects 53 of spatial structure present in species distributions except for their exact location within the study area, which is appropriate for testing the hypotheses. A total of 100 null model replications were analyzed.

Figure 12. Null model design. The area of occupancy of a species is estimated with various centroid-centered ellipses (A), the centroids are rotated at random angles until all centroids land within the country mask (B), and the space is filled to satisfy the original area of occupancy (C).

Categorical Null Models (CNM) Categorical null models were used to exclude the observed structure and effect of growth form in the dataset. The dataset of all 786 species was randomly sampled to create proportional growth form re-categorizations of the species. This process was repeated 1,000 times for vigorous null model sampling (Gotelli, 2001).

2.6.4 Significance testing

Preliminary analysis A hierarchal clustering analysis was performed using “Pvclust” package in R to visualize a set of first significant ecoregionalizations of Nicaragua. A multiscale bootstrap resampling was used to calculate an approximately unbiased p- value for each cluster (Suzuki and Shimodaira, 2006). 54

SDM data to Geographical Null Models Comparison of the observed results from the categorical wombling and the geographical null models was done to look for evidence for the presence of ecoregions. Two percentile tests were conducted for CBE rank compared to number of regions and superfluity values to understand the existence of ecoregions for woody and herbaceous growth forms, both taxonomically and phylogenetically. Percentile tests determine the position of a value relative to other values in a ranked dataset. Probability values (p-values) were calculated with set α at 0.025 level of significance (two-tailed) to determine the relevance of the findings.

SDM data to Categorical Null Models Comparison of the observed differences between woody and herbaceous growth forms and the categorical null models was done to look for evidence of differences in ecoregions between the two growth forms. Three percentile tests were conducted for CBE rank compared to number of regions, superfluity values and β- diversity values to understand ecoregion limits for woody and herbaceous growth forms, both taxonomically and phylogenetically. The same method was used as in the previous comparison.

Expectations for both of the study hypotheses were drawn. Under hypothesis one (H1) that herbaceous species demonstrate weaker ecoregion limits than woody species, the percentile test of CBE rank compared to number of regions would be expected to be higher for woody species than herbaceous (Figure 11, top row). Meanwhile, the percentile test of superfluity value per number of regions would be expected to be lower for woody species compared to herbaceous because lower superfluity values indicate more ecoregion-like borders (Figure 11, middle row). Lastly, 55 under hypothesis one (H1), β-diversity percentile test would be expected to display lower β-diversity values per CBE rank (Figure 11, bottom row). The contrast would be expected under hypothesis two (H2), where herbaceous taxa would show stronger ecoregion limits than woody taxa. Under H2, herbaceous taxa would have a higher number of regions compared to CBE rank, lower superfluity values per region, and higher β-diversity values per CBE rank when compared to woody taxa (Figure 11).

Effect Size Effect size measures the magnitude of the significant p-values. It is calculated by subtracting the growth form difference in SDM data to the mean of the CNMs. However, a superior measure corrects for dispersion: standardized effect size, which is the effect size divided by the standard deviation of the null models or interquartile range. By normalizing to standard deviation, the correct scale is applied (i.e., if the variation of the null models is large, the standard deviation is large). Thus the strength of differences becomes very apparent.

3 Results

3.1 Categorical wombling The country of Nicaragua has 6,543 grid cells at 5 km resolution, of which two cells lack any adjacent cells. These cells represent islands off the Atlantic coast, and were not considered in the categorical wombling analysis. A total of unique 12,716 CBEs are found between pairs of grid cells across Nicaragua (Figure 13). Preliminary exploration of results revealed that both herbaceous and woody datasets showed a splitting along the east-west axis of Nicaragua into ecoregional groupings roughly resemlbing Olson et al. 2001 but more finely cateogorized along the mountain areas (Figure 14). We then proceed to test whether ecoregions observed for the two growth forms were significant against geographic null models and whether ecoregions for different growth forms differed significantly from each other. 57

Figure 13. CBEs mapped on Nicaragua mask at 0.9- and 0.5-quantile shown for woody and herbaceous species for both β-diversity metrics. 58

Figure 14. Visualization of analysis for taxonomic (βsim) and phylogenetic (pβsim) β-diversity metric for both growth forms. Number of regions is indicated under each ecoregional division. The landscapes were not identified as significant under percentile tests, but represent the first splits of the landscape into wetter western and drier eastern regions. 59

3.1.1 Identifying ecoregions for growth forms To understand if ecoregions were real for different growth forms, the number of regions and superfluity boundary statistics between the observed values derived from the SDMs and the geographic null models were compared (Figure 15). A significant value in any analysis implies existence of ecoregions.

Results show that ecoregions found in herbaceous taxa using the taxonomic β-diversity metric are not significantly different to random, as demonstrated by the green line falling below the grey lines representing null models in Figure 15Aii. Significant ecoregions are discovered however using pβ- diversity metric in herbaceous (Figure 15Bii) across several CBE ranks. The opposite is true for woody species, where ecoregions are found in analysis with the taxonomic β- diversity metric and not with the pβ-diversity metric (Figure 15Ai, Bi respectively). Superfluity results demonstrate that boundaries are effectively formed when compared to random chance and further documenting the existence of ecoregions (Figure 15Aiii-iv & Biii-iv).

3.1.2 Identifying differences in ecoregions Observed differences between growth forms

To understand the ecoregional differences between growth forms, the number of regions, superfluity boundary statistic and β-diversity values between growth forms were compared were compared (Figures 16). Comparison of results from woody and herbaceous species strong support H1 in showing herbaceous taxa to have higher number of regions and β-diversity values at a given CBE rank compared to herbaceous taxa (Figure 16). Results from superfluity are harder to interpret, where superfluity values are clearly higher early on (i.e., lower number of regions) for woody species but lower later on (i.e., 60 higher number of regions) compared to herbaceous taxa (Figure 16).

Comparing SDM change to difference in Categorical Null Models

To statistically test for significant differences in the observed differences between herbaceous and woody taxa the observed differences between growth forms were compared to differences expected based on the categorical null models (Figure 17).

Results from the categorical wombling show higher rates of region formation and β-diversity values and lower superfluity values in woody compared to herbaceous species (Figure 17). These results are significant when compared to categorical null models (Figure 17). Woody taxa generally have stronger ecoregions than herbaceous taxa for the three percentile tests

(Regions, superfluity, β-diversity; Figure 17). Pβsim suggests less differences overall compared to βsim, meaning ecoregions are phylogenetically very similar for woody and herbaceous species. Standardized effect size is extremely high in taxonomic regions percentile test and percentile tests for β- diversity and pβ-diversity (Table 16), which signifies that there is much stronger difference between woody and herbaceous for regions formed and β-diversity than efficiency of boundaries. There is no overlap of significant values between percentile tests. 61

Figure 15. Graphs comparing the SDM data for woody (brown) and herbaceous (green) taxa versus the geographical null models (gray) to understand whether ecoregions exist. Two percentile tests are displayed: regions and superfluity. Significant p-values are shown as gray vertical lines at significant CBE rank or region. 62

Figure 16. Percentile tests for woody (brown) and herbaceous (green) SDM data. 63

Figure 17. Results of comparison between ΔSDM data and ΔCNMs (Woody – Herbaceous). 64

Table 15. The total number of significant p-values for each test and their corresponding CBE rank number, and region number for superfluity.

Regions Percentile Test Superfluity Percentile Test β-diversity Percentile Test Total Total Region Total CBE number CBE number CBE number number number number number 5502-34, 5759, 5802-85, 5897-8, 5927, 5930-1, 5933-37, 5947-50, 5952- 8886-7 (W) 6015, 6017, 6047-7239, 3000 9012 (H) 7242-69, 7271-81, 7289-

8236, 8308-72, 8416-7, 229, 231-506, 510-1, β 2701 8419-32, 8507-13, 8577- 2 348 513-9, 528, 535, sim 738, 9811-33, 9851-61, 540-1, 543-608

9865-991, 10852, 10879- 10054 (W) 4000 87, 10972-88, 11001, 10115 (H) 11039-44, 11385-410, 11788-92, 11844-95, 12182-8 pβ sim NA NA NA NA NA 594 71-669

Table 16. Top ten standardized effect size values for each percentile test and corresponding CBE rank number, and region number for superfluity. Highest value for each test is bolded.

Regions Percentile Test Superfluity Percentile Test β-diversity Percentile Test CBE Standardized Region Standardized Standardized CBE number CBE number number effect size number effect size effect size 6930-3, 6942-8, 8886-7 (W) 7.534965379 3000 0.003079 241 6.829438402 6952-3, 9012 (H) 6955 10054 (W) 6954 7.571711153 4000 0.002592 242 6.84836037 10115 (H) - - - - - 243 6.83558318 β sim - - - - - 247 6.839632332 - - - - - 248 6.961114248 - - - - - 249 6.939458247 - - - - - 250 6.967203665 - - - - - 251 6.924094175 - - - - - 252 6.944626729 - - - - - 256 6.873932617 NA NA NA NA NA 71 10.4967 NA NA NA NA NA 72 10.48242 NA NA NA NA NA 73 10.44742 NA NA NA NA NA 74 10.43099 pβ NA NA NA NA NA 106 10.39453 sim NA NA NA NA NA 108 10.45495 NA NA NA NA NA 112 10.44045 NA NA NA NA NA 121 10.43132 NA NA NA NA NA 122 10.44916 NA NA NA NA NA 128 10.38024 65

Regions percentile test

Regions percentile test shows in 2,701 significant CBEs for βsim and no significant CBEs in pβsim (Table 15, Figure 17Ai). This shows a random pattern in phylogeny with respect to geographic distributions. Woody species form stronger ecoregions than herbaceous with higher number of regions per CBE rank based on taxonomic β-diversity metric only. The highest standardized effect size occurs at CBE rank number 6954 (Table 16) and ecoregional formation was visualized at this value (Figure 18).

Figure 18. Visualization of regions formed for the highest standardized effect size in regions percentile test (CBE 6954).

Superfluity percentile test Superfluity percentile test shows two significant regions for

βsim and no significant CBEs in pβsim (Table 15, Figure 17Aii). Keeping in mind that high superfluity values infer weaker ecoregions and low superfluity values infer stronger ecoregions, SDM curve falling below the null distribution means woody taxa have stronger ecoregion boundaries and SDM curve above the null 66 distribution signifies herbaceous have stronger ecoregion boundaries. The highest standardized effect size occurs at region 3,000, which corresponds to CBE ranks 8,886 and 8,887 for woody taxa and CBE rank 9,012 for herbaceous taxa (Table 16) Ecoregional formation was visualized at this region (Figure 19).

Figure 19. Visualization of regions formed for highest standardized effect size value in superfluity percentile test (region 3,000).

Beta-diversity percentile test β-diversity percentile test shows in 348 significant CBEs for

βsim and 594 significant CBEs for pβsim (Table 15, Figure 17Aiii & 17Biii). Woody species have significantly stronger β-diversity values for the highest rank of CBEs for both metrics (Figure 17Aiii & Biii). In the β-diversity percentile test, the highest standardized effect size value occurred at CBE rank number 71 (Table 16). Ecoregion formation and location of highest β- diversity values was visualized at this value (Figure 20A & 20B, respectively). In the pβ-diversity percentile test, the highest standardized effect size value occurs at CBE rank number 250 67

(Table 16). Ecoregion formation and location of highest β- diversity values was visualized at this value (Figures 21A & 21B, respectively).

Figure 20. Visualization of ecoregions (A) and highest β-diversity values (B) for herbaceous and woody taxa at CBE rank 71, highest standardized effect size in β-diversity percentile test. 68

Figure 21. Visualization of ecoregions (A) and highest β-diversity values (B) for herbaceous and woody taxa at CBE rank 250, highest standardized effect size in pβ-diversity percentile test.

4 Discussion

4.1 General findings The true nature of ecoregions in space has remained untested thus far. Analyses using clustering methods have been used, but these have not focused on understanding and characterizing boundaries that delineate ecoregions, but rather, have focused on delineating ecoregions independent of whether they are delimited by continuous and/or significant boundaries in geographic space. Clustering methods have several pitfalls when applied to biogeographic data. First, regional evolutionary distinctiveness can be diluted by repeated episodes of interchange and NDMS ordination will detect transition zones as discrete even if their uniqueness is the effect of blending instead of independent evolutionary histories (Vermeij, 1991; Kreft and Jetz, 2010). Sometimes adjacent regions blend, so no single, clear line can be drawn between them due to geological and biological reasons. The clarity of boundaries between regions depends on the nature of the barrier and the floral history (Cox, 2001). Second, UPGMA algorithm is unable to differentiate between biogeographical core and transition zones (Kreft and Jetz, 2013; Holt et al., 2013b). In contrast, edge detection methods are a more robust method for locating exact boundaries of ecoregions in space and for measuring their width, shape and intensity.

In this thesis, the presence and nature of boundaries in woody and herbaceous taxa was formally tested using a boundary delineation method known as categorical wombling. Categorical data on species presence/absence across the entire area of Nicaragua was used to test whether ecoregions are real for different growth forms, and whether ecoregional categorizations 70 show significant differences between growth forms. Both taxonomic and phylogenetic β-diversity metric was used to explore ecoregions as simple floristic units and as evolutionary meta-communities sensu Pennington et al. (2009), respectively.

The area of Nicaragua was chosen as one of the best-known tropical floras thus far in terms of the completeness of the flora as well as the digitally available and georeferenced specimen data. Because most tropical areas remain incompletely sampled, SDMs were used to predict species distributions and community composition across the entire country using environmental predictors derived from radar-detected precipitation and interpolated weather station temperature data.

Results from this study clearly demonstrate that ecoregions are real for both herbaceous and woody taxa, and that they can be interpreted as evolutionary meta-communities. The fact that significant ecoregions were found for both growth forms allowed further exploration as to whether the ecoregions experiences by the two growth forms differed significantly from each other. Comparison between growth forms against the categorical null models clearly shows that herbaceous and woody taxa exhibit siginificantly different ecoregional patterns, regardless of β- diversity metric used. Woody taxa form more regions per CBE rank with stronger and more efficient borders (i.e., lower superfluity & higher β- and pβ-diversity values). Altogether, these results clearly support (H1) that herbaceous show weaker ecoregion limits because they have faster rates of climatic niche evolution (Smith and Beaulieu, 2009).

4.2 Why woody species show stronger ecoregions? Smith and Donoghue (2009) undoubtedly demonstrated that substitution rates differ between herbaceous and woody taxa. From their studies, it was found that these differences in substitution rates are reflected in species’ niche evolution, leading to faster rates of niche evolution in herbaceous compared to woody taxa. These findings and their implications to community composition and ecoregions has not been previously examined.

In this study, clear evidence was found to support that woody taxa show stronger ecoregions. These results could be explained by faster niche evolution of herbaceous species, and by faster dispersal rates shown by herbaceous taxa, which are both considered below.

High speciation and dispersal, relative to local factors, can affect adaptation leaving a profound historical and geographical imprint on the community composition (Ricklefs, 1987). Speciation may not necessarily culminate in evolutionary transformations across niche dimensions because plants may favor movement along corridors rather than immobility and obligatory evolution of key adaptations (Smith and Donoghue, 2008; Donoghue, 2008). Descendant species are likely to maintain approximately the same niches as their ancestors because closely related species tend to have similar ecological requirements, known as niche conservatism (Wiens, 2004; Wiens and Graham, 2005; Kozak and Wiens, 2006). “Phylogenetic niche conservatism” occurs when the similarity is higher than expected as based on relatedness (Losos, 2008). This common concept is extremely useful in ecology and evolution in understanding how lineages surpass substantial ecological boundaries and helping to explain why others have not (Donoghue and Edwards, 2014; Vilhena and 72

Antonelli, 2014). Woody lineages do not deviate much from ancestral climatic tolerances, whereas herbaceous species are better able to adapt to new environments (Smith and Beauleiu, 2009). Thus, woody species exhibit smaller niches, which could potentially lead to more and better-defined ecoregions.

Past and present climate is the primary determinant of plant range shifts but life history traits should also be considered (Normand et al., 2011). One of such factors is differing dispersal rates between woody and herbaceous taxa. Studies from Northern Hemisphere have found evidence of clear differences in dispersal rates between trees and herbs since last Glacial Maximum, where trees are still expanding their ranges (Higgins et al., 2003; Normand et al., 2011; Svenning and Skov, 2004, 2007a, b; Skov and Svenning, 2004; Ritchie and MacDonald, 1986). In the tropics, herbaceous species are often wind-dispersed, whereas trees and shrubs are more likely to be less efficiently dispersed by animals (Sugden, 1982). In addition, species with shared dispersal strategies but shorter generation time would be able to migrate more quickly (Feurdean et al., 2013). Such differences in dispersal rates would lead herbaceous species to show less overlap in their ranges in relation to ecoregions compared to woody species. This effect on species diversity could influence the ecoregionalization for different growth forms.

Nicaragua is placed at the junction of Mesoamerican montane species and South American lowland flora, where 90% of Central American angiosperms in lowland rainforests are estimated to be identical or derived from South American species (Gentry, 1982). This critical placement of Nicaragua has raised the question whether the northern highland ecoregions represent evolutionarily distinct lineages compared to the lowland flora. 73

This study could investigate this hypothesis because β-diversity metrics represent species composition dissimilarity between two communities (βsim) and the dissimilarity between two communities proportional to evolutionary time is based on phylogenetic composition (pβsim). Regrettably, our findings do not support this hypothesis and show significance for ecoregional difference in growth forms only for taxonomic β-diversity metric, leading to minimal interpretation of evolutionary ecoregional formation.

4.3 Study limitations and strengths Studies focusing on understanding changes in species composition across space rely on dense sampling. Globally, numerous parts remain under-collected for majority of taxa and reliable species ranges, the essential basis for potent diversity patterns analyses, are only applicable for a fragment of the earth’s landscape (Whittaker et al., 2005). Contiguous sampling points are most appropriate for identifying boundaries but spatial variability of species can often be patchy (Fagan et al., 2003; Kent et al., 2006). This study addressed the issue of incomplete sampling by the use of species distribution modeling methods based on the limited observations available across Nicaragua. SDMs have been used in other regional and macroscale studies, and if used properly, can accurately depict species diversity across broad scales (Zhang et al., 2016; Pineda and Lobo, 2009; Benito et al., 2013 – threshold choice). Relying on modelled species distribution could be seen as better than depending on expert based range maps such as used in mammal and bird studies. Studies have shown range maps produced by experts to fail in detecting finer scale patterns at resolutions below c. 200 km (Hulbert and Jetz, 2007). Spatial scale is critical to the accuracy of the delineated boundaries and their features, for both the geographical extent of the study area and for the grain 74

(i.e. resolution) of the data (Jacquez et al., 2000; Whittaker et al., 2005; Fagan et al., 2003, Fortin et al., 2000; Strayer et al., 2003; Kent et al., 2006). The study area extent should be large enough to assure sufficient data collection because if the area is too small, inaccurate patterns occur and obscure boundary location (Fagan et al., 2003). Contrastingly, if the extent is too big, multiple ecological processes can contribute to edge generation and result in noisy, inaccurate boundaries (Csillag et al., 2001). Therefore, this study focused on the relatively small-sized country of Nicaragua and at an adequate scale of c. 5 km.

Several methodological choices were utilized to assure the best possible models of species distribution were produced. This study chose to use a solitary machine-learning algorithm (MaxEnt) that permits the usage of differently constructed bias files. This algorithm is known to enhance model efficiency particularly in crudely collected areas and decrease overestimation common in SDM methods (e.g. Fourcade et al., 2014; Kramer-Schadt et al., 2013; Elith et al., 2010). By using a moderately conservative threshold, the 10-percentile of training presence points were forced to fall outside the threshold and thus suitable habitat regions were comprised of only 90% of the input model data. A decision threshold helps to aid model validation and interpretation by considering model output above the threshold as a prediction of presence (Pearson et al., 2006). Adjusting the threshold changes the proportion of the study scope predicted to be present thus affecting proportions of observed records that are successfully predicted (Pearson et al., 2006). Unfortunately, threshold independent validation statistics are incompatible with presence-only data (Pearson et al., 2006). For well-sampled regions with many 75 locality records, it is common to have a balance between false- positive and false-negative predictions and therefore a lower decision threshold, which would incorporate a larger predicted area (Pearson et al., 2006). No spatial filtering was conducted in order to retain data points that would otherwise be lost; although in retrospect spatial filtering could have been utilized, given the robustness of the models. Combination of bias models, recognized to diminish overprediction and boost model behavior, and a conservative threshold for every species habitat suitability characterization, predictions for species diversity and community composition should be rather robust.

One limiting factor in relying on modelled species distribution is the ability to include predictors such as soil variables known to affect species’ distribution. This study focused on using climatic variables to predict species ranges across Nicaragua, because climatic factors are seen as the primary factors that define species’ distributions (Kramer-Schadt et al., 2013). Soil factors are important but often define more local patterns of species distributions within the broader area of suitable climate (Svenning and Skov, 2005; Molina-Venegas et al., 2016). Accurately modelling species distribution, however, does rely on using a broad enough range of climatic factors in such cases, in order to fully capture species’ responses to climatic factors that define their distributions (Pottier et al., 2013). In this study, a broad range of 14 predictors was used to produce SDMs for the study species, in order to assure that specific combinations of climatic conditions for all species across the study range would be covered.

Another critical issue is the use of adequately realistic null models and spatial randomization procedures in studies such as those presented here (Jacquez et al., 2000). Null models are 76 needed to understand if the data is different from random chance. The geographical null model used in this study tried to keep much of the structure present in the data while disregarding the biological structure that would lead to aggregations of range size edges in space leading to the detection of ecoregions. While only 100 GNMs were used, the findings prove that ecoregions exist for both herbaceous and woody taxa. Preliminary analysis run during this study also show that ecoregions are real for all Nicaraguan species if run as a single dataset with 50 null models. Running 1,000 geographical null models would be better but given the time and computational constraints of this study, only 100 were completed.

4.4 Implications and further research Boundaries are the zones of greatest spatial change, thus providing valuable, relevant information to environmental management and biological conservation strategic development (Kent et al., 2006). Knowing region and boundary dynamics could be useful for central and applied research on the effects of spatial heterogeneity by highlighting areas of change and by ranking these rates of change by their magnitude, it is insightful to the degree of persistence or resilience of boundaries (Fortin and Drapeau, 1995).

Ecoregions can be useful even though they might not the same for all organisms and functional groups. This study allows a deeper understanding of some of the limitations of ecoregional classifications by demonstrating that different growth forms experience significantly different ecoregions in space. More studies will be needed to fully delimit and test ecoregions across scales and space so that global, more robust ecoregional classifications will be available for basic and applied sciences, as well as for conservation. Imperatively, 77 scrutinizing prominent and persuasive maps used to convey conservation themes, for instance the WWF Ecoregion map, has been characterized as “irresponsible” and “dissident”, but as Whittaker et al. (2005) revealed, the field of science should have the confidence to evaluate critically schematic diagrams like ecoregion differentiation in a vigorous scientific manner. Categorical charts present a streamlined translation of conservation targets and appeal to funding organizations, but they do not represent “the truth” scientifically and there is promise of continuing opportunities for data-driven studies, as expressed here. Continuing to enhance biogeographical studies and recommendations are needed.

Much remains to be developed in applying boundary detection methods for exploring ecoregions in space. The method is currently only available in R scripts written during this study, and no easily applicable software is available. Despite inherent flexibility of edge detection methods, there is a considerable methodological issue to be resolved, which is that statistically identified boundaries are not necessarily significant ones (Fagan et al., 2003). Boundary statistics and their interpretation against null models should be developed further in order to define methods that can be used to find “best” ecoregional landscapes. Because determining which significant value produces the “correct” ecoregion map cannot be distinguished, effective comparisons to the current WWF ecoregion map of Nicaragua (Figure 22) cannot be accomplished. For example, percentile tests of the boundary statistics suffer from inertia, where the values of neighboring points are not independent. The use of sliding window approaches commonly used in genetics could be explored. 78

Figure 22. Current understanding of ecoregions in Nicaragua as based on WWF Ecoregions (Olson et al., 2001).

5 Conclusion Ecoregions are a common concept in science and important in understanding the complexity and pattern of species diversity in space and time. They aim to delimit and describe geographic areas that are relatively uniform in biotic composition, and are used as baseline study units in ecological, evolutionary and climate change research. This study explores the existence of ecoregions as naturally occurring homogeneous units and tests whether these units differ for distinct growth forms using a boundary detection method known as categorical wombling, in which boundaries reflect spatial disjunctions in species composition. Both taxonomic and phylogenetic species turnover metrics are used to test for boundaries (a) as relevant biological units in ecological time scale, and (b) as evolutionary important meta-communities that portray congruence and affinity of communities through evolutionary time. Null models are used to test for significance in observed results. Hypothesis one (H1) states that herbaceous taxa show weaker ecoregion limits than woody and hypothesis two (H2) predicts that herbaceous species exhibit stronger ecoregion limits compared to woody. Results clearly demonstrate that ecoregions exist for both growth forms and that boundaries are greater and more efficient for woody taxa. Woody taxa have significantly more regions per CBE rank, lower superfluity values and higher β-diversity values compared to herbaceous taxa. These results are more apparent in β-diversity but still evident in phylogenetic β-diversity. All of the results combined show that woody taxa tend to form stronger ecoregions, supporting H1 that herbaceous taxa have weaker ecoregion limits. The different patterns exhibited by the growth forms could potentially be explained by differences in niche evolution and dispersal 80 ability. This study highlights some of the limitations of ecoregional classifications by demonstrating that significantly different ecoregions exist for distinct growth forms. Ecoregions can still be useful to conservation strategies and management, even though they might not the same for all organisms and functional groups, as long as the scientific community acknowledges these constraints.

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Appendix 1

Acalypha alopecuroidea Jacq. Anthurium clavigerum Poepp. Acalypha arvensis Poepp. Anthurium consobrinum Schott Acalypha diversifolia Jacq. Anthurium obtusum (Engl.) Grayum Acalypha leptopoda Müll. Arg. Anthurium scandens R. Sheffer Acalypha schiedeana Schltdl. Anthurium upalaense Croat & R.A. Baker Achimenes longiflora DC. Antigonon guatemalense Meisn. Acidoton nicaraguensis (Hemsl.) G.L. Webster Apeiba tibourbou Aubl. Aciotis rubricaulis (Mart. ex DC.) Triana Aphelandra scabra (Vahl) Sm. Acmella papposa (Hemsl.) R.K. Jansen Apoplanesia paniculata C. Presl Acmella radicans (Jacq.) R.K. Jansen Arachnothryx (Benth.) Planch. buddleioides Acnistus arborescens (L.) Schltdl. Ardisia compressa Kunth Adelia triloba (Müll. Arg.) Hemsl. Ardisia nigropunctata Oerst. Adiantum concinnum Humb. & Bonpl. ex Ardisia opegrapha (Mez) Pipoly & Willd. Ricketson Adiantum macrophyllum Sw. Ardisia revoluta Kunth Adiantum petiolatum Desv. Arenaria lanuginosa (Michx.) Rohrb. Adiantum tetraphyllum Humb. & Bonpl. ex Aristolochia anguicida Jacq. Willd. Adiantum trapeziforme L. Arrabidaea patellifera (Schltdl.) Sandwith Adiantum wilsonii L. Arthrostemma ciliatum Pav. exD. Don Aechmea pubescens Baker Arundinella deppeana Nees exSteud. Aegiphila panamensis Moldenke Asclepias curassavica L. Aeschynomene americana L. Asclepias similis Hemsl. Ageratum conyzoides L. Asclepias woodsoniana Standl.&Steyerm. Ageratum petiolatum (Hook. & Arn.) Hemsl. Aspasia epidendroides Lindl. Alibertia edulis (Rich.) A. Rich. ex Asplenium auritum Sw. DC. Allophylus racemosus Sw. Asplenium formosum Willd. Alternanthera pubiflora (Benth.) Kuntze Asplenium serratum L. Alternanthera sessilis (L.) R. Br. ex DC. Asterogyne martiana (H. Wendl.) H. Wendl. ex Hemsl. Alvaradoa amorphoides Liebm. Ayenia dentata Brandegee Amaioua glomerulata (Lam. ex Poir.) Ayenia micrantha Standl. Delprete & C.H. Perss. Amaranthus spinosus L. Baccharis trinervis Pers. Amphilophium paniculatum (L.) Kunth Baltimora recta L. Ananthacorus (Sw.) Underw. & Maxon Banara guianensis Aubl. angustifolius Begonia cardiocarpa Liebm. Andira inermis (W. Wright) Kunth ex DC. Begonia glabra Aubl. Anemia phyllitidis (L.) Sw. Begonia plebeja Liebm. Angostura granulosa (Kallunki) Kallunki Begonia semiovata Liebm. Annona reticulata L. Begonia sericoneura Liebm. Anoda cristata (L.) Schltdl. Besleria laxiflora Benth. Anthephora hermaphrodita (L.) Kuntze Bidens pilosa L. Anthurium bakeri Hook. f. 105

Blakea (Cogn.) Penneys & Capparis flexuosa (L.) L. maurofernandeziana Almeda Blakea watsonii (Cogn.) Penneys & Capparis indica (L.) Druce Almeda Capparis pachaca (C. Wright ex Radlk.) Blechnum appendiculatum Willd. Iltis Blechnum occidentale L. Capraria biflora L. Bocconia frutescens L. Capsicum annuum L. Boerhavia coccinea Mill. Carex polystachya Sw. ex Wahlenb. Boerhavia erecta L. Carica papaya L. Bolbitis portoricensis (Spreng.) Hennipman Cascabela ovata (Cav.) Lippold Bonellia nervosa (C. Presl) B. Ståhl & Casearia aculeata Jacq. Källersjö Casearia corymbosa Kunth Bourreria andrieuxii (DC.) Hemsl. Casearia sylvestris Sw. Brassavola nodosa (L.) Lindl. Casearia tremula (Griseb.) Griseb. ex Bravaisia integerrima (Spreng.) Standl. C.Wright Brickellia diffusa (Vahl) A. Gray Casimiroa sapota Oerst. Brosimum alicastrum Sw. Cassia grandis L. f. Browallia americana L. Cassipourea elliptica (Sw.) Poir. Buchnera pusilla Kunth Castilleja arvensis Schltdl.& Cham. Buddleja americana L. Catopsis nutans (Sw.) Griseb. Bursera simaruba Sarg. Cavendishia bracteata (Ruiz & Pav. ex J. St.-Hil.) Hoerold Byrsonima crassifolia (L.) Kunth Cayaponia racemosa (Mill.) Cogn. Byttneria aculeata (Jacq.) Jacq. Cecropia peltata L. Caesalpinia coriaria (Jacq.) Willd. Cedrela odorata L. Caesalpinia exostemma DC. Celtis iguanaea (Jacq.) Sarg. Caesalpinia velutina (Britton & Rose) Cenchrus pilosus Kunth Standl. Calathea lutea (Aubl.) Schult. Centradenia (Schltdl. & Cham.) G. inaequilateralis Don Calathea macrosepala K. Schum. Centrosema pubescens Benth. Calathea marantifolia Standl. Cestrum aurantiacum Lindl. Calathea micans (L. Mathieu) Körn. Cestrum nocturnum L. Calea urticifolia (Mill.) DC. Cestrum tomentosum L. f. Calliandra calothyrsus Meisn. Chamaecrista diphylla (L.) Greene Calliandra houstoniana (Mill.) Standl. Chamaecrista nictitans (L.) Moench Calopogonium caeruleum (Benth.) C. Wright Chamaedorea costaricana Oerst. Calopogonium mucunoides Desv. Chamissoa acuminata Sohmer Calycophyllum (Vahl) DC. Chamissoa altissima Suess. candidissimum Calyptrocarya (Brongn.) Urb. Chaptalia nutans (L.) Pol. glomerulata Chelonanthus acutangulus (Ruiz & Pav.) Gilg Campyloneurum (Sw.) Fée angustifolium Chiococca alba (L.) Hitchc. Campyloneurum (Lodd. ex Link) Link brevifolium Chomelia spinosa Jacq. Canavalia brasiliensis Mart. exBenth. Cinnamomum triplinerve (Ruiz & Pav.) Kosterm. Canavalia villosa Benth. Cirsium mexicanum DC. Canna indica L. Cissampelos pareira L. Caperonia palustris (L.) A. St.-Hil. 106

Cissampelos DC. Coursetia caribaea (Jacq.) Lavin tropaeolifolia Cissus microcarpa Vahl Coussapoa villosa Poepp. & Endl. Citharexylum caudatum L. Crateva tapia L. Clematis acapulcensis Hook. &Arn. Crescentia alata Kunth Cleome spinosa Jacq. Crossopetalum (Hemsl.) Lundell parviflorum Clibadium surinamense L. Crotalaria incana L. Clidemia capitellata (Bonpl.) D. Don Crotalaria sagittalis L. Clidemia dentata Pav. ex D. Don Crotalaria verrucosa L. Clidemia hirta (L.) D. Don Croton draco (Klotzsch) G.L. Webster Clidemia octona (Bonpl.) L.O. Williams Croton niveus Jacq. Clidemia sericea D. Don Croton schiedeanus Schltdl. Clidemia setosa (Triana) Gleason Cupania glabra Sw. Clusia cooperi Standl. Cuphea carthagenensis (Jacq.) J.F. Macbr. Clusia minor L. Cuphea utriculosa Koehne Cnestidium rufescens Planch. Curatella americana L. Cnidoscolus urens (L.) Arthur Cyathea multiflora Sm. Coccocypselum hirsutum Bartl.exDC. Cynanchum rensonii (Pittier) Woodson Coccoloba acuminata Kunth Cyperus articulatus L. Coccoloba caracasana Meisn. Cyperus ligularis L. Coccoloba floribunda (Benth.) Lindau Cyperus luzulae (L.) Rottb. ex Retz. Cochlospermum vitifolium (Willd.) Spreng. Cyperus odoratus L. Cojoba sophorocarpa (Benth.) Britton & Rose Cyperus surinamensis Rottb. Columnea nicaraguensis Oerst. Dalbergia retusa Baill. Columnea purpurata Hanst. Dalea cliffortiana Willd. Combretum farinosum Kunth Dalechampia scandens L. Combretum fruticosum (Loefl.) Stuntz Daphnopsis americana (Griseb.) Nevling Commelina diffusa Burm. f. Davilla kunthii A. St.-Hil. Commelina erecta L. Davilla nitida (Vahl) Kubitzki Conostegia subcrustulata (Beurl.) Triana Delilia biflora (L.) Kuntze Conostegia xalapensis (Bonpl.) D. Don ex DC. Dendropanax arboreus (L.) Decne. & Planch. Convolvulus nodiflorus Desr. Desmodium adscendens (Sw.) DC. Conyza canadensis (Nutt.) Cronquist Desmodium barbatum (L.) Benth. Conyza laevigata (Rich.) Pruski Desmodium incanum (Sw.) DC. Conyza sumatrensis (S.F. Blake) Pruski & G. Desmodium infractum DC. Sancho Cordia alliodora (Ruiz & Pav.) Oken Desmodium nicaraguense Oerst. Cordia dentata Poir. Desmopsis schippii Standl. Cordia panamensis L. Riley Dialium guianense (Aubl.) Sandwith Cornutia pyramidata L. Dichaea panamensis Lindl. Cosmos caudatus Kunth Dichorisandra amabilis J.R. Grant Costus pulverulentus C. Presl Dicranoglossum panamense (C. Chr.) L.D. Gómez Costus scaber Ruiz & Pav. Dieffenbachia oerstedii Schott 107

Digitaria bicornis (Lam.) Roem. & Schult. Ficus insipida Willd. Dimerandra emarginata (G. Mey.) Hoehne Ficus maxima Mill. Diodia apiculata (Willd.) K. Schum. Ficus obtusifolia Kunth Diodia teres Walter Ficus pertusa L. f. Diospyros salicifolia Humb. & Bonpl. ex Willd. Fimbristylis dichotoma (L.) Vahl Drymaria cordata (L.) Willd. ex Schult. Fimbristylis littoralis Gaudich. Drymonia serrulata (Jacq.) Mart. Florestina latifolia (DC.) Rydb. Echinochloa colona (L.) Link Forsteronia spicata G. Mey. Echites yucatanensis Millsp.ex Standl. Fuchsia paniculata Lindl. Eclipta prostrata (L.) L. Funastrum clausum (Jacq.) Schltr. Eichhornia crassipes (Mart.) Solms Galactia striata (Jacq.) Urb. Elaphoglossum peltatum (Sw.) Urb. Galinsoga quadriradiata Ruiz &Pav. Eleocharis elegans (Kunth) Roem. & Schult. Galphimia speciosa C.E. Anderson Eleocharis montana (Kunth) Roem. & Schult. Garcinia intermedia (Pittier) Hammel Elephantopus mollis Kunth Genipa americana L. Elytraria imbricata (Vahl) Pers. Geonoma congesta H. Wendl.ex Spruce Encyclia papillosa (Bateman) Ag.-Olav. Geonoma deversa (Poit.) Kunth Epidendrum nocturnum Jacq. Gliricidia sepium (Jacq.) Kunth ex Walp. Epidendrum radicans Pav. exLindl. Gnaphalium attenuatum DC. Erechtites (L.) Raf. ex DC. Godmania aesculifolia (Kunth) Standl. hieraciifolius Erechtites (Link ex Spreng.) DC. Gomphrena serrata L. valerianifolius Gonolobus barbatus Kunth Eriosema diffusum (Kunth) G. Don Gonzalagunia panamensis (Cav.) K. Schum. Eryngium foetidum L. Gouania lupuloides (L.) Urb. Erythrina berteroana Urb. Gouania polygama (Jacq.) Urb. Erythrina steyermarkii Krukoff&Barneby Gronovia scandens L. Esenbeckia berlandieri (Donn. Sm.) Kaastra Guaiacum sanctum L. Espejoa mexicana DC. Guarea excelsa Kunth Eupatorium odoratum L. Guarea grandifolia DC. Eupatorium pycnocephalum Less. Guatteria amplifolia Triana & Planch. Eupatorium vitalbae DC. Guazuma ulmifolia Lam. Euphorbia dioeca Kunth Guettarda macrosperma Donn. Sm. Euphorbia graminea Jacq. Guzmania lingulata (L.) Mez Euphorbia heterophylla L. Guzmania monostachia (L.) Rusby ex Mez Euphorbia hirta L. Guzmania nicaraguensis Mez & C.F. Baker Euphorbia hyssopifolia L. Gyrocarpus americanus Jacq. Euphorbia lasiocarpa Klotzsch Hamelia axillaris Sw. Euphorbia thymifolia L. Hamelia longipes Standl. Evolvulus alsinoides (L.) L. Hamelia patens Jacq. Faramea occidentalis (L.) A. Rich. Hasseltia floribunda Kunth Ficus aurea Nutt. Heliconia aurantiaca Ghiesbr. Ficus crassinervia Desf. exWilld. 108

Heliconia latispatha Benth. Ipomoea trifida (Kunth) G. Don Heliconia mathiasiae G.S. Daniels &F.G. Stiles Iresine calea (Ibáñez) Standl. Heliconia tortuosa Griggs Iresine diffusa Humb. & Bonpl. ex Willd. Heliconia vaginalis Benth. Iresine nigra Uline & W.L. Bray Helicteres guazumifolia Kunth Isertia haenkeana DC. Heliocarpus Turcz. Isocarpha oppositifolia (DC.) D.J. Keil & Stuessy appendiculatus Heliocarpus mexicanus (Turcz.) Sprague Jaltomata repandidentata (Dunal) Hunz. Heliotropium indicum L. Jatropha curcas L. Herissantia crispa (L.) Brizicky Jatropha gossypiifolia L. Heteranthera limosa (Sw.) Willd. Justicia aurea Schltdl. Heteranthera reniformis Ruiz & Pav. Justicia carthagenensis Jacq. Heterocentron (Link & Otto) A. Braun & Kallstroemia maxima (L.) Hook. & Arn. subtriplinervium C.D. Bouché Kallstroemia pubescens (G. Don) Dandy Heteropterys laurifolia (L.) A. Juss. Karwinskia calderonii Standl. Hirtella racemosa (Willd.) Prance Kohleria spicata (Kunth) Oerst. Hoffmannia gesnerioides (Oerst.) Kuntze Kosteletzkya depressa (L.) O.J. Blanch.; Fryxell & Hybanthus attenuatus (Humb. & Bonpl. ex Willd.) D.M. Bates Schulze-Menz Lacistema aggregatum (P.J. Bergius) Rusby Hydrocotyle mexicana Ging. Lagascea mollis Cav. Hydrolea spinosa (Brand) L.J. Davenp. & A. Pool Lantana trifolia L. Hymenachne amplexicaulis (Rudge) Nees Lantana urticifolia Mill. Hymenaea courbaril L. Laportea aestuans (L.) Chew Hypericum pratense Schltdl.& Cham. Lasiacis nigra Davidse Hypolytrum longifolium (Liebm.) T. Koyama Lasiacis procerrima (Hack.) Hitchc. Hypoxis decumbens L. Lasiacis ruscifolia (Swallen) Davidse Hyptis capitata Jacq. Lasianthaea fruticosa (L.) K.M. Becker Hyptis mutabilis (Rich.) Briq. Leandra granatensis Gleason Hyptis pectinata (L.) Poit. Lepidaploa salzmannii (DC.) H. Rob. Hyptis suaveolens (L.) Poit. Lepidium costaricense Thell. Hyptis verticillata Jacq. Leucaena shannonii (Standl. ex Britton & Rose) Hyptis vilis Kunth & C.D. Bouché Zárate Licania arborea Seem. Ichnanthus pallens (Sw.) Munro ex Benth. Lindackeria laurina C. Presl Indigofera suffruticosa Mill. Lindernia crustacea (L.) F. Muell. Indigofera trita (Roth) de Kort & G. Thijsse Lippia cardiostegia Benth. Inga oerstediana Benth. ex Seem. Lippia myriocephala Schltdl.& Cham. Inga punctata Willd. Lobelia laxiflora Kunth Inga sapindoides Willd. Lobelia xalapensis Kunth Inga vera Willd. Loeselia ciliata L. Ipomoea batatas (L.) Lam. Lonchocarpus (Poir.) DC. Ipomoea carnea (Mart. ex Choisy) D.F. heptaphyllus Austin Lonchocarpus Donn. Sm. Ipomoea hederifolia L. minimiflorus Ipomoea nil (L.) Roth Lonchocarpus Benth. phaseolifolius 109

Lonchocarpus Standl.&Steyerm. Merremia umbellata (L.) Hallierf. phlebophyllus Ludwigia hyssopifolia (G. Don) Exell Mesechites trifidus (Jacq.) Müll. Arg. Ludwigia octovalvis (Jacq.) P.H. Raven Miconia albicans (Sw.) Steud. Ludwigia peploides (Kunth) P.H. Raven Miconia argentea (Sw.) DC. Ludwigia peruviana (L.) H. Hara Miconia hondurensis Donn. Sm. Luehea candida (DC.) Mart. Miconia impetiolaris Naudin Luehea seemannii Triana&Planch. Miconia lacera (Bonpl.) Naudin Luehea speciosa Willd. Miconia nervosa (Sm.) Triana Lycianthes multiflora Bitter Microgramma (L.) Copel. lycopodioides Lycopodiella cernua (L.) Pic. Serm. Microgramma percussa (Cav.) de la Sota Lygodium venustum Sw. Mikania micrantha Kunth Lysiloma divaricatum (Jacq.) J.F. Macbr. Milleria quinqueflora L. Mabea occidentalis Benth. Mimosa albida (Willd.) B.L. Rob. Maclura tinctoria (L.) D. Don ex Steud. Mimosa pigra (A. Gray ex Torr.) B.L. Turner Macroptilium (Moc. & Sessé ex DC.) Urb. Mirabilis violacea (L.) Heimerl atropurpureum Macroscepis pleistantha Donn. Sm. Mitracarpus hirtus (L.) DC. Malachra alceifolia Jacq. Mollinedia viridiflora Tul. Malachra fasciata Jacq. Mollugo verticillata L. Malpighia glabra L. Monotropa uniflora L. Malvastrum americanum (L.) Torr. Monstera adansonii (Schott) Madison Malvaviscus arboreus Cav. Monstera siltepecana Matuda Mandevilla hirsuta (Rich.) K. Schum. Montanoa hibiscifolia Benth. Mandevilla subsagittata (Ruiz & Pav.) Woodson Montanoa tomentosa (Sch. Bip. ex K. Koch) V.A. Funk Manettia reclinata L. Morella cerifera (L.) Small Manihot aesculifolia (Kunth) Pohl Morinda panamensis Seem. Manilkara chicle (Pittier) Gilly Mouriri myrtilloides (Benth.) Morley Maranta arundinacea L. Mucuna holtonii (Kuntze) Moldenke Margaritaria nobilis L. f. Muntingia calabura L. Margaritopsis microdon (DC.) C.M. Taylor Myriocarpa longipes Liebm. Mariosousa centralis (Britton & Rose) Seigler & Myriocarpa obovata Donn. Sm. Ebinger Marsypianthes chamaedrys (Vahl) Kuntze Myrospermum frutescens Jacq. Martynia annua L. Nectandra nitida Mez Maxillaria Rchb. f. Nectandra salicifolia (Kunth) Nees friedrichsthalii Maxillaria neglecta (Schltr.) L.O. Williams Neea fagifolia Heimerl Maxillaria uncata Lindl. Neea laetevirens Standl. Melampodium divaricatum (Rich.) DC. Neomarica variegata (M. Martens & Galeotti) Henrich & Goldblatt Melanthera nivea (L.) Small Neurolaena lobata (L.) Cass. Melochia tomentosa L. Niphidium crassifolium (L.) Lellinger Melothria pendula L. Notopleura polyphlebia (Donn. Sm.) C.M. Taylor Merremia quinquefolia (L.) Hallierf. Notopleura uliginosa (Sw.) Bremek. 110

Ocimum campechianum Mill. Phyla nodiflora (L.) Greene Ocotea veraguensis (Meisn.) Mez Phyllanthus amarus Schumach. & Thonn. Odontadenia macrantha (Willd. ex Roem. & Schult.) Physalis ignota Britton Markgr. Odontonema tubaeforme (Bertol.) Kuntze Phytolacca icosandra L. Olyra latifolia L. Phytolacca rivinoides Kunth & C.D. Bouché Oplismenus burmannii (Vasey) McVaugh Picramnia antidesma (DC.) W.W. Thomas Oplismenus hirtellus (Lam.) Mez ex Ekman Pilea hyalina Fenzl Oreopanax capitatus (Jacq.) Decne. & Planch. Pilea microphylla (L.) Liebm. Ouratea lucens (Kunth) Engl. Piper aduncum L. Palicourea guianensis Aubl. Piper amalago L. Palicourea triphylla DC. Piper auritum Kunth Panicum trichoides Sw. Piper hispidum Sw. Paragonia pyramidata (Rich.) Bureau Piper jacquemontianum Kunth Paspalum conjugatum P.J. Bergius Piper marginatum Jacq. Passiflora bicornis Houst.exMill. Piper peltatum L. Passiflora biflora Lam. Piper tuberculatum Jacq. Passiflora foetida L. Piper umbellatum L. Passiflora seemannii Griseb. Piper urophyllum C. DC. Passiflora vitifolia Kunth Pisonia aculeata L. Passovia pyrifolia (Kunth) Tiegh. Pisonia macranthocarpa (Donn. Sm.) Donn. Sm. Paullinia cururu L. Pithecellobium dulce (Roxb.) Benth. Paullinia fuscescens Kunth Pithecellobium (Humb. & Bonpl. ex Willd.) lanceolatum Benth. Pausandra trianae (Müll. Arg.) Baill. Pithecoctenium (L.) A.H. Gentry crucigerum Pavonia schiedeana Steud. Pleopeltis angusta Humb. & Bonpl. ex Willd. Pehria compacta (Rusby) Sprague Pleopeltis crassinervata (Fée) T. Moore Pentaclethra macroloba (Willd.) Kuntze Pluchea carolinensis (Jacq.) G. Don Peperomia galioides Kunth Plumbago zeylanica L. Peperomia glabella (Sw.) A. Dietr. Polyclathra cucumerina Bertol. Peperomia obtusifolia (L.) A. Dietr. Polygala paniculata L. Peperomia pellucida (L.) Kunth Polymnia maculata Cav. Peperomia tetraphylla Hook. & Arn. Polypodium lindenianum Kunze Peperomia urocarpa Fisch. & C.A.Mey. Polypodium plebeium Schltdl.& Cham. Petiveria alliacea L. Polypodium polypodioides Weath. Pharus latifolius L. Polystachya foliosa (Hook.) Rchb. f. Phaseolus lunatus L. Polystemma guatemalense (Schltr.) W.D. Stevens Phenax hirtus (Sw.) Wedd. Portulaca pilosa L. Philodendron hederaceum (Jacq.) Schott Posoqueria latifolia (Rudge) Schult. Philodendron radiatum Schott Pouzolzia occidentalis (Liebm.) Wedd. Philodendron tenue K. Koch & Augustin Prescottia stachyodes (Sw.) Lindl. Phlebodium pseudoaureum (Cav.) Lellinger Priva lappulacea (L.) Pers. Phoradendron (Kunth) Griseb. Prockia crucis P. Browneex L. quadrangulare 111

Prosthechea abbreviata (Schltr.) W.E. Higgins Renealmia cernua (Sw. ex Roem. & Schult.) J.F. Macbr. Prosthechea chacaoensis (Rchb. f.) W.E. Higgins Renealmia mexicana Klotzschex Petersen Prosthechea fragrans (Sw.) W.E. Higgins Rhipsalis baccifera (Sol.) Stearn Prosthechea ochracea (Lindl.) W.E. Higgins Rhynchosia minima (L.) DC. Protium confusum (Rose) Pittier Rhynchospora cephalotes (L.) Vahl Pseudelephantopus (Juss. ex Aubl.) C.F. Baker Rhynchospora nervosa T. Koyama spicatus Pseuderanthemum (Nees) Radlk. Richardia scabra L. cuspidatum Psidium guineense Sw. Rinorea dasyadena A. Robyns Psittacanthus (Benth.) Kuijt Rinorea deflexiflora Bartlett rhynchanthus Rinorea squamata S.F. Blake Psychotria berteroana DC. Rivina humilis L. Psychotria brachiata Sw. Rubus urticifolius Poir. Psychotria chagrensis Standl. Ruellia blechum L. Psychotria cyanococca Dombrain Ruellia inundata Kunth Psychotria deflexa DC. Ruellia paniculata L. Psychotria elata (Sw.) Hammel Ruellia terminalis (Nees) Wassh. Psychotria graciliflora Benth. Russelia sarmentosa Jacq. Psychotria grandis Sw. Rytidostylis gracilis Hook. & Arn. Psychotria horizontalis Sw. Salvia misella Kunth Psychotria marginata Sw. Salvia occidentalis Sw. Psychotria nervosa Sw. Samanea saman (Jacq.) Merr. Psychotria panamensis (K. Krause) C.W. Ham. Sapindus saponaria L. Psychotria poeppigiana Müll. Arg. Sapium glandulosum (L.) Morong Psychotria pubescens Sw. Sapranthus violaceus (Dunal) Saff. Psychotria racemosa Rich. Saurauia waldheimii Buscal. Psychotria subsessilis Benth. Sauvagesia erecta L. Psychotria suerrensis Donn. Sm. Scaphyglottis prolifera (Sw.) Cogn. Psychotria tenuifolia Sw. Schistocarpha (Fenzl) Kuntze Pteris altissima Poir. eupatorioides Pterocarpus rohrii Vahl Schoepfia schreberi J.F. Gmel. Quararibea funebris W.S. Alverson Schultesia lisianthoides (Griseb.) Benth. & Hook. f. ex Hemsl. Quassia amara L. Scleria latifolia Sw. Quercus sapotifolia Liebm. Scleria melaleuca Rchb. ex Schltdl. & Cham. Quercus segoviensis Liebm. Sclerocarpus divaricatus (Benth.) Benth. & Hook. f. ex Hemsl. Randia aculeata L. Scoparia dulcis L. Randia armata (Sw.) DC. Selaginella arthritica Alston Randia nicaraguensis Lorence&Dwyer Selaginella pallescens (C. Presl) Spring Rauvolfia tetraphylla L. Selaginella sertata Spring Ravenia rosea Standl. Semialarium mexicanum (Miers) Mennega Rehdera trinervis (S.F. Blake) Moldenke Senna atomaria (L.) H.S. Irwin & Barneby Reinhardtia gracilis (H. Wendl.) Drude ex Dammer Senna cobanensis (Britton) H.S. Irwin & Reinhardtia simplex (H. Wendl.) Drude ex Dammer Barneby 112

Senna hayesiana (Britton & Rose) H.S. Irwin Stegnosperma cubense A. Rich. & Barneby Senna hirsuta H.S. Irwin & Barneby Stemmadenia donnell- (Rose) Woodson smithii Senna obtusifolia (L.) H.S. Irwin & Barneby Stemmadenia pubescens Benth. Senna occidentalis (L.) Link Stenospermation Hemsl. angustifolium Senna pallida (Micheli) H.S. Irwin & Stigmaphyllon retusum Griseb. Barneby Senna papillosa (Britton & Rose) H.S. Irwin Stromanthe hjalmarssonii (Körn.) Petersen ex K. & Barneby Schum. Senna skinneri (Benth.) H.S. Irwin & Struthanthus orbicularis (Kunth) Blume Barneby Senna undulata (Benth.) H.S. Irwin & Struthanthus quercicola (Schltdl. & Cham.) Blume Barneby Stylogyne turbacensis (Oerst.) Ricketson & Pipoly Senna uniflora (Mill.) H.S. Irwin & Barneby Symphonia globulifera L. f. Serjania rhombea Radlk. Symphyotrichum subulatum (Michx.) G.L. Nesom Serpocaulon triseriale (Sw.) A.R. Sm. Synechanthus H. Wendl. Sesbania herbacea (Mill.) McVaugh warscewiczianus Setaria liebmannii E. Fourn. Synedrella nodiflora (L.) Gaertn. Setaria parviflora (Poir.) Kerguélen Syngonium angustatum Schott Sicydium tamnifolium (Kunth) Cogn. Syngonium podophyllum Schott Sida cuspidata (A. Robyns) Krapov. Tabebuia ochracea (A.H. Gentry) A.H. Gentry Sida rhombifolia L. Tabebuia rosea (Bertol.) DC. Sideroxylon capiri (Pittier) T.D. Penn. Tabernaemontana alba Mill. Simarouba amara Aubl. Tabernaemontana Jacq. amygdalifolia Sinningia incarnata (Aubl.) D.L. Denham Talinum paniculatum (Jacq.) Gaertn. Siparuna thecaphora (Poepp. & Endl.) A. DC. Talinum triangulare (Jacq.) Willd. Smilax domingensis Willd. Tecoma stans (L.) Juss. ex Kunth Smilax spinosa Mill. Tectaria mexicana (Fée) C.V. Morton Solanum americanum Mill. Tectaria panamensis (Hook.) R.M. Tryon & A.F. Tryon Solanum hayesii Fernald Telanthophora (Less.) H. Rob. & Brettell Solanum hazenii Britton grandifolia Teramnus uncinatus (L.) Sw. Solanum lanceifolium Jacq. Terminalia oblonga (Ruiz & Pav.) Steud. Solanum nudum Dunal Tetracera volubilis L. Solanum rugosum Dunal Tetragastris panamensis (Engl.) Kuntze Solanum torvum Sw. Thelypteris (E. Fourn.) C.V. Morton Solanum volubile Sw. nicaraguensis Thouinidium decandrum (Bonpl.) Radlk. Souroubea sympetala Delpino Tibouchina longifolia (Vahl) Baill. Spananthe paniculata Jacq. Tillandsia bulbosa Hook. Spathiphyllum Schott friedrichsthalii Tillandsia fasciculata Sw. Spermacoce ocymifolia Willd. ex Roem. & Schult. Tillandsia recurvata (L.) L. Spigelia humboldtiana Cham. & Schltdl. Tillandsia schiedeana Steud. Spiracantha cornifolia Kunth Tillandsia tricolor Schltdl.& Cham. Spondias mombin L. Tillandsia usneoides (L.) L. Spondias purpurea L. Tournefortia glabra L. Stachytarpheta frantzii Pol. Tournefortia L. 113

hirsutissima Varronia bullata L. Tradescantia zanonia (L.) Sw. Varronia curassavica Jacq. Tradescantia zebrina Heynh. Varronia guanacastensis (Standl.) J.S. Mill. Tragia volubilis L. Varronia inermis (Mill.) Borhidi Trema micrantha (L.) Blume Varronia macrocephala Desv. Trichilia americana (Sessé & Moc.) T.D. Penn. Varronia spinescens (L.) Borhidi Trichilia havanensis Jacq. Verbena litoralis Kunth Trichilia martiana C. DC. Verbesina turbacensis Kunth Trichilia pallida Sw. Vernonia patens Kunth Trichilia quadrijuga (C. DC.) T.D. Penn. Viburnum hartwegii Benth. Trichocentrum ascendens (Lindl.) M.W. Chase & N.H. Vigna vexillata (L.) A. Rich. Williams Trichospermum (A. Rich.) Kosterm. Viguiera cordata (Hook. & Arn.) D'Arcy grewiifolium Virola sebifera Aubl. Tridax procumbens L. Vismia baccifera (L.) Triana & Planch. Trigonia rugosa Benth. Vismia macrophylla Kunth Trigonidium egertonianum Batemanex Lindl. Vitis tiliifolia Humb. & Bonpl. ex Schult. Triolena hirsuta (Benth.) Triana Vochysia ferruginea Mart. Tripogandra serrulata (Vahl) Handlos Vriesea heliconioides (Kunth) Hook. ex Walp. Triumfetta bogotensis DC. Waltheria indica L. Triumfetta lappula L. Wedelia acapulcensis Kunth Trixis inula Crantz Wigandia urens (Ruiz & Pav.) Kunth Trophis mexicana (Liebm.) Bureau Wissadula excelsior (Cav.) C. Presl Trophis racemosa (L.) Urb. Witheringia meiantha (Donn. Sm.) Hunz. Turbina corymbosa (L.) Raf. Witheringia solanacea L'Hér. Turpinia occidentalis Croat Xiphidium caeruleum Aubl. Urera baccifera (L.) Gaudich. ex Wedd. Xylophragma seemannianum (Kuntze) Sandwith Urera caracasana (Jacq.) Gaudich. ex Griseb. Xylosma flexuosa (Kunth) Hemsl. Urochloa fusca (Sw.) B.F. Hansen & Wunderlin Zapoteca formosa (Kunth) H.M. Hern. Vachellia collinsii (Saff.) Seigler & Ebinger Zeltnera quitensis (Kunth) G. Mans. Vachellia farnesiana (L.) Wight & Arn. Ziziphus guatemalensis Hemsl. Vachellia pennatula (Schltdl. & Cham.) Seigler & Ebinger Zygia longifolia (Humb. & Bonpl. ex Willd.) Valeriana candolleana Gardner Britton & Rose