1 Phylogenetic patterns of rarity in a regional species pool of tropical woody plants

2 M. Isabel Loza, Iván Jiménez, Peter M. Jørgensen, Gabriel Arellano, Manuel J. Macía, 3 Vania W. Torrez, and Robert E. Ricklefs

4 M. Isabel Loza: Department of Biology, University of Missouri, St. Louis, MO, 63121, 5 USA and Herbario Nacional de Bolivia, Campus Universitario Cota-Cota, calle 27, 6 Correo Central Cajón Postal 10077, La Paz, Bolivia. [email protected] 7 Iván Jiménez: Center for Conservation and Sustainable Development, Missouri 8 Botanical Garden, P.O. Box 299, St. Louis, Missouri 63166, USA; and Department of 9 Biology, University of Missouri, St. Louis, MO, 63121, USA. 10 [email protected] 11 Peter M. Jørgensen: Missouri Botanical Garden, P.O. Box 299, St. Louis, Missouri 12 63166, USA. [email protected] 13 Gabriel Arellano: Center for Tropical Forest Science – ForestGEO, Smithsonian 14 Tropical Research Institute.NMNH-MRC 166, West Loading Dock, 10th and 15 Constitution Ave. NW, Washington, DC 20560, USA. 16 [email protected] 17 Manuel J. Macía: Departamento de Biología, Área de Botánica, Universidad 18 Autónoma de Madrid, Calle Darwin 2, ES–28049 Madrid, Spain. 19 [email protected] 20 Vania W.Torrez: Division of Plant Conservation and Population Biology, Department 21 of Biology, University of Leuven, B-3001 Leuven, [email protected] 22 Robert E. Ricklefs: Department of Biology, University of Missouri, St. Louis, MO, 23 63121, USA. [email protected] 24 25 Running title: Phylogenetic patterns of rarity 26 27 Keywords: Andean floras, Bolivia, habitat breadth, geographical range size, local 28 abundance, Madidi, rarity, phylogenetic conservatism, phylogenetic signal. 29 30 Send all correspondence to: M. Isabel Loza. [email protected] 31 32 Type of article : Research paper 33 Words in abstract: 294 34 Words in main body (from the Introduction through Biosketch): 5409 35 Words in references : 1354 (52 references) 36 Number of figures: 7 37 Number of tables: 0 38 Number of appendices: 3 39 40 41 42 43 44 45 46 ABSTRACT 47 48 Aim Rarity, which is believed to influence extinction risk, can be defined in terms of 49 local abundance, geographic range size, and habitat breadth. Phylogenetic patterns in 50 these attributes provide insight into the extent to which rarity and extinction risk are 51 conserved during evolution, and the potential for species-level heritability. We 52 evaluated phylogenetic signal (i.e., related species resembling each other more than 53 species drawn at random) and evolutionary conservatism (similarity among related 54 species exceeding that expected from a Brownian model of evolution) in three axes of 55 rarity (local abundance, geographic range size, and habitat breadth) among species in a 56 regional pool of tropical woody plants. 57 Location The Madidi region in Bolivia. 58 Time period 2001 to 2010. 59 Major taxa studied Lignophyta clade. 60 Methods We used a network of 48 1-ha forest plots and 442 0.1-ha forest plots to 61 measure local abundance and habitat breadth of 1,700+ woody plant species (from 62 100+ plant families). We estimated geographic range size from occurrence records of 63 individual species across the Neotropics. We characterized overall phylogenetic 64 patterns of rarity using Blomberg’s K and applied variance partitioning among 65 taxonomic levels, as well as disparity analysis, to describe patterns of trait distribution 66 at different depths in the phylogeny. 67 Results We found phylogenetic signal, but not evolutionary conservatism, in the three 68 axes of rarity. The variance in rarity among supra-specific taxa, particularly families 69 and genera, exceeded that calculated from random draws of species from the Madidi 70 region. Phylogenetic signal, estimated by the proportion of variance among supra- 71 specific taxonomic levels, varied between 23 and 36% for local abundance and 72 geographic range size, and between 9 and 10% for habitat breadth. 73 Main conclusions The regional pool of woody plant species in Madidi exhibits 74 phylogenetic signal in rarity that is consistent with biologically significant species- 75 level heritability.

76 77 INTRODUCTION 78 Many authors have defined rarity in terms of the local abundance of individuals 79 and geographic range size (see Gaston 1994 for a review), with habitat breadth often 80 included as a third axis (Rabinowitz et al., 1986; McGill 2011). Each of these three 81 axes of rarity is a species-level trait that describes a key aspect of abundance and 82 distribution thought to influence extinction risk, though at different scales: small local 83 populations are vulnerable to demographic stochasticity and environmental variation 84 (Lande 1993; Aitken et al., 2008); species with small geographic ranges are 85 particularly susceptible to adverse conditions occurring simultaneously across their 86 entire extent (McKinney 1997; Gaston 2003; Harnik et al., 2012); species that occupy 87 fewer habitats are more vulnerable to environmental change (McKinney 1997; 88 Biesmeijer et al., 2006; Colles et al., 2009). 89 Studies concerning the determinants of species abundance and distribution 90 often focus on ecological factors, leaving aside the evolution of traits that may 91 determine rarity. Phylogenetic patterns in local abundance, geographic range size, and 92 habitat breadth provide insight into the evolution of traits that determine rarity and the 93 factors that influence extinction risk (Jones et al., 2005). Furthermore, the extent to 94 which the axes of rarity are phylogenetically conserved or labile determines, in part, 95 how extinction risk is distributed across a given phylogeny, and assesses the amount of 96 uniquely shared evolutionary history under threat (Purvis 2008). Likewise, because 97 phylogenetic signal includes species-level heritability of traits (sensu Housworth et al., 98 2004), phylogenetic patterns of rarity indicate the extent to which rarity is heritable at 99 the species-level (Jablonski 1987; 2008; Ricklefs & Latham 1992; Waldron 2007; 100 Borregaard et al., 2012). 101 Some previous analyses have shown that rarity is more similar among closely 102 related species than expected by chance (Jablonski 1987; Ricklefs & Latham 1992; 103 Jones et al., 2005; Waldron 2007; Menken et al., 2009; Leao et al., 2014; Mouillot & 104 Gaston 2009; Machac et al., 2011), while others have supported the opposite 105 conclusion (Gaston 2003; Ricklefs, 2010; 2011). These different outcomes may be 106 explained in at least two ways, each deserving of further inquiry. First, phylogenetic 107 patterns of rarity might be largely idiosyncratic, reflecting peculiarities of different 108 study systems (Jones et al., 2005). To examine this possibility, studies of phylogenetic 109 patterns of rarity in major groups of organisms other than well-studied vertebrates as 110 birds and carnivores, would be informative. We know of only three analyses of the 111 phylogenetic structure of distribution and abundance in tropical plants (Ricklefs 2010; 112 Leao et al., 2014; Dexter & Chave 2016). Yet, tropical plants represent a sizable 113 portion of diversity on Earth, and include many species with poor dispersal abilities 114 and small geographic ranges (Gaston 2003; Ghalambor et al., 2006). These 115 characteristics might promote similarity in rarity among closely related species (Jones 116 et al., 2005), in which case phylogenetic patterns of rarity in tropical plants might 117 differ from those in other organisms, such as birds, carnivores, or extra-tropical plants. 118 Second, most previous analyses have not been designed to examine general 119 phylogenetic patterns of rarity, particularly when these patterns might vary with the 120 phylogenetic or taxonomic level at which they are measured (Jones et al., 2005; 121 Machac et al., 2011). Most have used metrics that quantify patterns of rarity across 122 entire phylogenies, such as Blomberg’s K (Blomberg et al., 2003) or Pagel’s λ (Pagel 123 1999). In addition, few studies have addressed the hierarchical distribution of rarity 124 within a phylogeny, as may be done using taxonomically nested analyses or node-by- 125 node analyses (but see Jones et al., 2005; Machac et al., 2011). Combining these 126 different metrics might reveal whether particular evolutionary patterns of rarity are 127 common only at certain taxonomic levels or phylogenetic depths. Likewise, 128 phylogenetic patterns of rarity might depend on the particular axis of rarity being 129 examined. This possibility could be directly evaluated by estimating phylogenetic 130 patterns in the three axes of rarity in the same study system(s). Yet, few empirical 131 studies have done so (Ricklefs 2010; 2011). 132 Here, we tested the hypothesis that the three axes of rarity (local abundance, 133 geographic range size, and habitat breadth) in tropical plants exhibit phylogenetic 134 signal and evolutionary conservatism. We assessed phylogenetic signal at different 135 levels of phylogenetic relationship using a whole-phylogeny metric as well as two 136 approaches that describe patterns at different phylogenetic depths. We used an 137 extensive network of forest plots (48 large plots and 442 small plots) located in the 138 Madidi region of the Bolivian Andes, which encompasses extensive topographic and 139 environmental variability and hosts rare and widespread species. We measured local 140 abundance and habitat breadth of 1,700+ woody species; we also use occurrence 141 records across the Neotropics to quantify geographic range size. We found 142 phylogenetic signal in the three axes of rarity: closely related species had more similar 143 population characteristics than expected from a null model that randomly assigned 144 rarity values to species. These results suggest that extinction risk may be elevated in 145 some clades of the Madidi species phylogeny. 146 147 METHODS 148 Field sampling 149 We focused on woody plants (including trees, shrubs, lianas, palms, and tree 150 ferns) of the Madidi region of Bolivia, located in the northeastern slope of the Bolivian 151 Andes. The Madidi region encloses a wide range of vegetation types and 152 environmental conditions (see Fuentes 2005 for a review), which goes from 200−6000 153 m in elevation. 154 Between 2001 and 2010, 490 vegetation plots were established at 200−4500 m 155 of elevation distributed in many tropical forest habitats (Fig. 1). Forty-eight large plots 156 were 1-ha (100×100 m) in area and 442 small plots were 0.1-ha (100×10 m or 50×20 157 m) in area. In each large plot, all plant stems with a diameter at breast height (DBH, at 158 1.3 m above ground level) ≥10 cm were recorded and tagged. For the small plots, all 159 stems with DBH ≥2.5 cm were recorded, but not tagged. All plots were within closed- 160 canopy mature forest with no sign of recent disturbance, and each plot was at least 250 161 m from the nearest other plot. Each stem was assigned to a morphospecies in the field. 162 Vouchers of every morphospecies were deposited at multiple herbaria (including the 163 Herbario Nacional de Bolivia in La Paz [LPB] and the Missouri Botanical Garden in 164 St. Louis [MO]), and voucher information was digitized in Tropicos® 165 (http://tropicos.org/ ). 166 Taxonomy and dated phylogeny 167 In 2011, we identified vouchers and undertook a comprehensive appraisal with 168 the help of specialists to ensure that species names were applied consistently across all 169 plots. The taxonomy followed the Angiosperm Phylogeny Website (APW, Stevens 170 2001 onwards) and Tropicos®. Our analyses included only individuals that were 171 determined to currently accepted species, and excluded morphospecies as well as 172 stems determined only to supra-specific taxonomic ranks. Altogether the large plots 173 contained 28,409 individuals representing 806 accepted species in 100 families, and 174 the small plots contained 112,492 individuals representing 1,729 accepted species in 175 134 families. We excluded 692 morphospecies (29% of all species) from the small 176 plots and 224 (22%) from the large plots, because they could not be assigned to named 177 species. Additionally, 246 individuals from the large plots (1%) and 5,801 from the 178 small plots (5%) were excluded because they remained unidentified. 179 A phylogeny resolved to the species level was not available for the species in 180 the study region. We therefore constructed a phylogeny that included all extant 181 Lignophyta families (angiosperms, gymnosperms, and ferns) as terminal taxa (Fig. S1 182 in appendix S1), based on tree R20120829 in Phylomatic version 3 (Webb & 183 Donoghue 2005), revised following APW. We compiled published estimates of node 184 ages from APW for 74% of the 426 nodes in the Lignophyta family-level phylogeny. 185 Because single nodes were often assigned different ages by different studies (Table S2 186 in appendix S1), we explored the effect of uncertainty in node age estimates by 187 conducting analyses using three ultrametric trees based on the minimum (youngest), 188 median, and maximum (oldest) ages for each node. For each of these three 189 alternatives, we obtained pseudo-chronograms using the BLADJ function of Phylocom 190 version 4.2 (Webb et al., 2011). 191 Measuring the three axes of rarity 192 For each species, we estimated local abundance, geographic range size, and 193 habitat breadth (Tables S3−4 in appendix S2), using data from large and small plots 194 separately because the species pools included in each type of plot did not fully overlap. 195 Local abundance was calculated as the sum of all individuals divided by the number of 196 all same-sized plots in which a species occurred. Geographic range size was calculated 197 as the extent of occurrence (EOO), a measure of the spatial spread of the occurrences 198 of a species. This is not a measure of the area over which a species actually occurs, but 199 it is rather the geographic extent of the species (Gaston & Fuller 2009). We estimated 200 EOO as the area of the minimum convex polygon enclosing all known occurrence 201 points (following IUCN 2014, Joppa et al., 2015). For each species, all georeferenced 202 records of occurrence in the Neotropics were downloaded from Tropicos®. Because 203 Tropicos houses the Madidi data set, the taxonomic match between vegetation plot 204 data and specimen data is well curated. We excluded records for which the geographic 205 coordinates of the specimen and the description of the collecting locality did not match 206 the country of origin. Habitat breadth was quantified as the number of habitats in 207 which a species was known to occur within the Madidi region, following standardized 208 vegetation types delineated to represent major biotic responses to bioclimatic, 209 geomorphological, and edaphic features in the study region (Navarro & Maldonado 210 2002, Fig. 1). 211 Because the distributions of local abundance, geographic range size, and 212 habitat breadth showed marked positive skew (Table S5 in appendix S3), we log- 213 transformed these variables to approximate normal distributions for further analysis. 214 The three axes of rarity were weakly correlated, with Spearman’s correlation 215 coefficients less than 0.21 (Fig. S6 in Appendix S3). 216 Measuring phylogenetic patterns of rarity 217 We defined phylogenetic signal in each axis of rarity as the tendency of related 218 species to resemble each other more than species drawn at random from the same 219 phylogeny (Blomberg et al., 2003; Münkemüller et al., 2012). The null model used to 220 test this hypothesis, known as “tip randomization”, randomly assigns species trait 221 values across a given phylogeny, so that trait values have no memory of their ancestry 222 and any similarity among species due to shared ancestry is eliminated. Following 223 Losos (2008), we defined phylogenetic conservatism (and, conversely, lability) as 224 more (less) phylogenetic trait clustering than expected from a Brownian (random 225 evolution) null model of evolution (Felsenstein 1985). Using these definitions, 226 phylogenetic signal is necessary but insufficient to demonstrate phylogenetic 227 conservatism. 228 We described phylogenetic patterns of rarity by combining metrics from three 229 approaches: (i) a whole-phylogeny metric (Blomberg’s K, Blomberg et al., 2003), (ii) 230 an analysis designed to describe patterns at different phylogenetic depths (variance 231 partitioning among taxonomic levels, Hadfield & Nakagawa 2010), and (iii) an 232 analysis designed to describe patterns on a node-by-node basis (disparity analysis, 233 Harmon et al., 2003). The first approach tested for both phylogenetic signal and 234 phylogenetic conservatism, while the others tested only for phylogenetic signal. 235 The first approach was based on Blomberg’s K, a metric that is scaled by the 236 expected K value under a Brownian model of evolution, so that K<1 ( K>1) implies that 237 relatives resemble each other less (more) than expected under a Brownian model of 238 evolution (Blomberg et al., 2003). We calculated K based on the family-level 239 phylogeny described above, and random samples of one species per family. We draw 240 1,000 random samples to obtain a distribution of 1,000 observed K values. To test for 241 phylogenetic signal and conservatism, we compared each of the 1,000 observed K 242 values against 10,000 iterations of the tip randomization and Brownian null models. 243 We emphasize that this first approach, based on Blomberg’s K, quantifies phylogenetic 244 patterns in rarity among species that belong to different plant families, but not among 245 confamilial species. 246 Our second approach quantified patterns of rarity among species within genera 247 and did not require a dated phylogeny. Rather it was based on partitioning variance 248 among nested random effects representing hierarchical taxonomic levels (Hadfield & 249 Nakagawa 2010). The proportion of variance explained by supra-specific taxonomic 250 levels provides an estimate of “phylogenetic heritability” ( sensu Housworth et al., 251 2004) that is analogous to Pagel’s λ (Hadfield & Nakagawa 2010). Following Prinzing 252 et al. (2001), we tested for phylogenetic signal by comparing observed variation within 253 hierarchical taxonomic levels to expected values according to a tip randomization null 254 model. We calculated 95% confidence intervals for expected variation within each 255 hierarchical taxonomic level as the interval between the 2.5 and 97.5 percentiles of 256 10,000 iterations of the null model. Then, we examined whether observed values fell 257 within these confidence intervals. Additionally, to know which genera and families 258 were more common or rarer than expected by the tip randomization null model (in 259 terms of the three axes of rarity), for each genus and family we compared observed 260 fitted values to the respective distribution of fitted values from the 10,000 null model 261 iterations. 262 The third approach involved disparity analysis, which tested for phylogenetic 263 signal at each node of the phylogeny, using all the species in the datasets. For this 264 analysis, we added polytomies for genera and species at the tips of the family-level 265 phylogeny described above. Disparity is the average Euclidean distance in trait space 266 between pairs of species within a clade (Harmon et al., 2003). Relative disparity for a 267 clade descending from a given node within a broader phylogeny is calculated by 268 dividing its disparity by the average disparity across the whole phylogeny. Relative 269 disparity values <1 imply that clades contain relatively little of the variation present 270 across the phylogeny as a whole and, consequently most variation is found between 271 clades (rather than within clades). Conversely, values >1 imply that most of the 272 variation across the phylogeny is contained within clades. To test for phylogenetic 273 signal, we calculated observed relative disparity at each node of the phylogeny, and 274 compared it to expected values derived from 50,000 iterations of the tip randomization 275 null model. 276 All the comparisons between distributions of observed and expected values 277 were based on Holm-Bonferroni p-adjusted values to account for multiple 278 comparisons. All analyses were performed in R version 3.0.2 (R Development Core 279 Team, 2013). 280 281 RESULTS 282 The first approach we used to describe phylogenetic patterns of rarity, based on 283 Blomberg’s K, quantified phylogenetic patterns in rarity only among species that 284 belong to different plant families. It did not reveal statistically significant phylogenetic 285 signal or conservatism (Fig. 2 and Figs. S7−8 in Appendix S3). Observed Blomberg’s 286 K values were always indistinguishable from those generated by the tip randomization 287 null model and, except in the case of local abundance in the large plots, always lower 288 than the values generated by the Brownian model of evolution (Fig. 2 and Figs. S7−8). 289 Our second approach to describing phylogenetic patterns of rarity was variance 290 partitioning among taxonomic levels, including confamilial and congeneric species. It 291 revealed statistically significant phylogenetic signal in all three axes of rarity. Even 292 though most of the variance in each of the axes of rarity was concentrated among 293 species within genera, variance among supra-specific taxonomic levels always 294 exceeded that produced by the tip randomization null model (Fig. 3). Phylogenetic 295 heritability, estimated by the proportion of variance explained by supra-specific 296 taxonomic levels, was 23−36% for local abundance and geographic range size, and 297 9−10% for habitat breadth (Fig. 3). For the three axes of rarity, the proportion of 298 variance associated with a taxonomic family was always greater than expected by the 299 tip randomization null model. The same was true for genera with the exception of 300 habitat breadth in the large plots. 301 The comparisons between the observed fitted values and the expected fitted 302 values revealed that both large and small plots included families and genera that were 303 more common or rarer than expected by chance. For the large plots, only 4 % of the 304 families and 9.6 % of the genera were rarer than expected by chance in at least in one 305 of the three axes of rarity examined here (Table S9, Figs S10−11 in appendix S3). 306 Conversely, for the small plots 32.8 % of the families and 22 % of the genera were 307 found to be rarer than expected by chance on at least one of the rarity axes, and 2.2% 308 of the families and 2.3 % of the genera were rarer than expected by chance on all three 309 axes (Table S12, Figs S13−14 in appendix S3). Interestingly, most families in the 310 asterid clade were rarer than expected by chance on at least one rarity axis (Figs. 311 4a−5a and Figs. S10−11 and S13−14), and some (e.g., Gesneriaceae, Campanulaceae, 312 and Macgraviaceae) were rare on all three axes (Fig. 5a). Rarity within the asterid 313 clade exhibits a strong pattern of phylogenetic signal in our study region. 314 Disparity analysis indicated that clades originating at various depths in the 315 phylogeny exhibited phylogenetic signal in habitat breadth and geographic range size 316 (disparity < 0.8; p ≤ 0.05). For the large plots, habitat breadth exhibited phylogenetic 317 signal in the clade formed by the families Boraginaceae, Lamiaceae, Bignoniaceae, 318 and Verbenaceae (Fig. 6). For the small plots, geographic range size exhibited 319 phylogenetic signal in several nodes of the Rosales clade (involving Rhamnaceae, 320 Cannabaceae, Urticaceae, Moraceae, and Ulmaceae), in the nodes subtending Laurales 321 and Magnoliales (including Monimiaceae, Hernandiaceae, Lauraceae, Siparunaceae, 322 Myristicaceae, Magnoliaceae, and Annonaceae), and in the node subtending two 323 monocot families Arecaceae and Poaceae (Fig. 7). These patterns were partly 324 consistent with those revealed by the variance partitioning analysis: the clades that 325 revealed phylogenetic signal with the disparity analysis (described above) 326 also contained families that were more common than expected by chance with respect 327 to geographic range size (Fig. 5) and rarer than expected by chance with respect to 328 habitat breadth (Fig. 6), reaffirming that these clades exhibit phylogenetic signal. 329 Disparity analysis also revealed some clades that exhibit high phylogenetic 330 lability in local abundance (Fig. 7). In particular, analyses based on the small plots 331 showed that relative disparity was higher than expected by the tip randomization null 332 model in the nodes subtending the clade formed by two monocot families (Arecaceae 333 and Poaceae; disparity = 8.5; p ≤ 0.05; Fig 7). 334 335 DISCUSSION 336 We examined phylogenetic patterns of rarity within a regional pool of tropical 337 woody plants and found phylogenetic signal in local abundance, habitat breadth, and 338 geographic range size, in the sense that variation among supra-specific taxa exceeded 339 that expected by chance. Embedded in this overall pattern, a few clades exhibited high 340 lability in local abundance. Overall, our results support the hypothesis that local 341 abundance, geographic range, and habitat breadth are more similar among closely 342 related species than expected by chance. Below, we explore implications of these 343 findings and discuss caveats with respect to our analyses. 344 Detection of phylogenetic patterns of rarity 345 Null models are central to our ability to infer phylogenetic patterns of rarity. 346 Metrics that compare rarity (local abundance, habitat breadth and geographic range 347 size in this case) across all species in a phylogeny (e.g., Blomberg’s K), or between 348 sister species, are compared to values expected under various null models (Krasnov et 349 al., 2011; Machac et al., 2011; Waldron 2007). However, to our knowledge this is the 350 first use of an explicit null model (tip-randomization) to assess phylogenetic patterns 351 of rarity based on variance partitioning across taxonomic levels. Previous studies have 352 suggested that rarity is phylogenetically labile because it is most variable among 353 species within genera (Gaston 2003; Ricklefs 2010, 2011), a result similar to this 354 study. However, previous studies did not explicitly use null models to test the 355 significance of phylogenetic signal or conservatism. Our variance partitioning analysis 356 showed that the three axes of rarity were most variable among species within genera, 357 but supra-specific taxonomic levels always explained more variance than expected by 358 the tip randomization null model (Fig. 3), indicating phylogenetic signal. Thus, our 359 study highlights the usefulness of interpreting variance partitioning across taxonomic 360 levels with explicit null models when one is testing for phylogenetic patterns of rarity. 361 While the analysis of variance across taxonomic levels detected phylogenetic 362 signal (Fig. 3), the analysis based on Blomberg’s K failed to detect phylogenetic signal 363 or conservatism (Fig. 2). Two considerations might contribute to this difference. One 364 is that the analysis based on Blomberg’s K did not include all species. In particular, we 365 generated 1,000 samples by randomly choosing one species per family and, based on 366 each of those samples, measured Blomberg’s K (see Methods). This, in turn, might 367 have decreased the precision of estimates of Blomberg’s K, which is thought to 368 decrease with the degree of incomplete sampling in phylogenies (Münkemüller et al., 369 2012). Moreover, incomplete sampling in our analysis was not random. Instead, we 370 designed our analysis to include only one species per family. Thus, the analysis based 371 in Blomberg’s K could not detect phylogenetic signal in rarity among confamilial and 372 congeneric species. Yet, the phylogenetic signal detected by the analysis of variance is 373 largely due to similarity among confamilial species (Fig. 3). 374 A second potential issue is that, with whole-phylogeny metrics such as 375 Blomberg’s K, opposite patterns at different phylogenetic depths might obscure each 376 other (Machac et al., 2011). For example, analyses of local abundance in the small 377 plots detected phylogenetic signal among confamilial and congeneric species (Fig. 3), 378 but also high lability at deeper levels including a few clades corresponding to families 379 or groups of families (Fig 7). This shows that phylogenetic patterns of rarity may 380 depend on the phylogenetic scope over which they are measured (Jones et al., 2005; 381 Machac et al., 2011). 382 Biological significance of phylogenetic patterns of rarity 383 We detected significant phylogenetic signal in rarity, but not phylogenetic 384 conservatism as defined here relative to a Brownian model of evolution (Losos 2008). 385 More detailed phylogenetic data might uncover conservatism in rarity among con- 386 familial species, and this should be a goal for future studies. However, the level of 387 phylogenetic signal detected here might be biologically significant in the context of 388 species-level heritability (Jablonski 2008), even in the absence of phylogenetic 389 conservatism. Absence of phylogenetic conservatism does not imply absence of 390 species-level heritability. Indeed, species-level heritability can be defined as the 391 tendency of closely related species to be more similar than expected by chance 392 (Jablonski 2008), which matches the definition of “phylogenetic heritability” 393 (Housworth et al., 2004; Hadfield & Nakagawa 2010) as well as the definition of 394 phylogenetic signal adopted here. 395 The processes underlying this pattern of phylogenetic signal are poorly 396 understood. At least three hypotheses could explain the pattern. (i) Phylogenetic signal 397 in rarity could follow phylogenetic signal in other biological variables such as body 398 size, dispersal ability, habitat requirements, and other life history traits (Gaston 2003). 399 It might also be partly determined by phylogenetic signal in vulnerability to negative- 400 density dependence imposed by pathogens (Gilbert & Webb 2007), although these 401 interactions could potentially evolve rapidly (Ricklefs 2010; 2011). (ii) Closely related 402 species may share broad-scale geographic domains and, thus, temporal and spatial 403 environmental templates that may largely determine species rarity (Mouillot &Gaston 404 2009; Machac et al. , 2011). (iii) Given directional trends between range size and 405 species age (Pigot et al. , 2012), clades characterized by high levels of recent 406 diversification would include a higher proportion of species with small ranges than 407 clades characterized by lower levels of recent diversification. Thus phylogenetic signal 408 in geographic range would emerge in regional species pools that include clades that 409 diversified extensively and recently as well as clades that did not. This third hypothesis 410 is consistent with a negative relationship between species diversity, on one hand, and 411 range size and abundance, on the other (Dexter & Chave 2016). 412 Phylogenetic scope and patterns of rarity in tropical plants 413 The strength of phylogenetic signal or conservatism may depend on the 414 phylogenetic scope used to describe patterns of rarity (see discussion above, Jones et 415 al., 2005; Machac et al., 2011). Thus, it is important to describe the phylogenetic 416 scope of studies focused on regional species pools so that the results are interpreted 417 accordingly, and proper comparisons established. The most basal nodes in the dated, 418 family-level phylogenies that we used reach into the Paleozoic, separating ferns from 419 seed plants and gymnosperms from angiosperms (Figs. 4a and 5a). However, the 420 regional pool of woody species in Madidi includes few non-angiosperm species, and it 421 is mostly composed of clades that diverged from each other no more than 110 Ma 422 (Figs. 4b and 5b), i.e., postdating the crown age of angiosperms according to most 423 estimates (Magallón et al., 2015). Point estimates for the age of the most derived nodes 424 in the phylogenies that we used in this analysis are largely >20 Ma (Figs. 4b and 5b), 425 reflecting the fact that divergence of angiosperm families is generally thought to 426 predate the Neogene (Magallón et al., 2015). Nonetheless, we detected phylogenetic 427 signal due to similarity of confamilial species of unknown age. In lieu of information 428 on the ages of many angiosperm families, current appraisals of the origin of 429 Neotropical plant diversity suggest that a large fraction of the extant species diversity, 430 and perhaps genus diversity, arose during the Mid-to-Late Miocene or more recently 431 (Hughes et al., 2013), including the Pliocene and Pleistocene in the case of Andean 432 species (Hughes & Atchison 2015). Hence, our findings likely describe phylogenetic 433 patterns of rarity that range mostly from the Late Cretaceous through the Neogene. 434 We are aware of only three previous studies of phylogenetic patterns of rarity 435 in tropical plants that span a phylogenetic scope similar to that of our study. These 436 studies focused on one or two axes of rarity (as opposed to three in our study). Leao et 437 al., (2014) found evidence of phylogenetic signal in geographic range size measured as 438 extent of occurrence and area of occupancy among 6,929 species from the Atlantic 439 forest of Brazil, representing 112 angiosperm families (Pagel’s λ 0.54, 0.57 440 respectively). Dexter & Chave (2016) demonstrated phylogenetic signal in geographic 441 range size and also local abundance among 631 Amazonian angiosperm tree genera 442 (Pagel’s λ 0.37, 0.32 respectively). The third study employed variance partitioning 443 across taxonomic levels to examine phylogenetic signal in local abundance among 250 444 tree species representing 53 angiosperm families in a 50-ha plot in Panama (Ricklefs 445 2010). Although this latter study did not use an explicit null model to test the 446 significance of phylogenetic signal (see above discussion), it found that 32% of the 447 variance occurred among supra-specific taxonomic levels. Further studies are needed 448 before generalizations about phylogenetic patterns of rarity can be drawn for regional 449 pools of tropical plant species. Nonetheless, the studies currently available suggest that 450 regional pools of tropical plant species might often be characterized by phylogenetic 451 signal in rarity. 452 Implications for conservation 453 Some evidence (e.g., for mammals, crinoids, and foraminifera) suggests that 454 species distributed across more types of habitats and that tolerate wider ranges of 455 environmental conditions have higher persistence (Colles et al 2009). Also, geographic 456 range size, followed by habitat breadth, appears to play an important role in the 457 survival of marine animal species (Harnik et al., 2012; Saupe et al., 2015); extant 458 species have significantly larger geographic ranges than extinct species (Saupe et al., 459 2015). In this sense, rarity has been associated with differential persistence of species 460 during times of background levels of extinction as well as during mass extinction 461 events (Jablonski 1986; 2008; Colles et al., 2009; Harnik et al., 2014; Saupe et al., 462 2015). Thus, the pattern of phylogenetic signal described here (Fig. 3 and Fig. 5−7) 463 may partly determine the outcomes of biotic impacts of ongoing climate change on 464 Andean floras. Temperature in the study region has increased by 0.2−0.3 °C per 465 decade during the last 30 years, and these rates increase at higher elevations (Vuille & 466 Bradley 2000). Species with small population sizes, narrow habitat breadths, and 467 restricted geographic distributions are likely to be more threatened by climate change 468 than species with larger populations and broader habitat breadths and geographic 469 ranges (Aitken et al., 2008). If that were the case, then our results showing 470 phylogenetic signal in rarity would suggest that extinction risk from climate change 471 would be phylogenetically clustered across Madidi woody plants. Accordingly, some 472 clades, such as Asterids (Figs. 4a−5a and Figs. S10−11 and S13−14), would have a 473 particularly high proportion of threatened species, so the amount of evolutionary 474 history under threat could be higher than expected by chance (Purvis 2008). 475 Caveats 476 Our analyses might have been affected by the exclusion of morphospecies 477 (species without a specific epithet) from the analysis. Morphospecies accounted for 478 29% and 22% of all named plus unnamed species in the 0.1 ha and 1 ha plots, 479 respectively. The potential impact of this exclusion depends on the distribution of 480 morphospecies across the phylogeny. If the morphospecies were randomly distributed 481 across the phylogeny, then we would not expect the analysis to be strongly biased. 482 Alternatively, if morphospecies were not randomly distributed across the phylogeny, 483 and if they tended to have low (or high) values of rarity, then the potential for bias in 484 our analysis would be higher. Additionally, we excluded from the analysis individuals 485 that were not determined to the species level. Some of these individuals may have 486 belonged to species included in the analysis, in which case, habitat breadth and local 487 abundance might have been underestimated for some species. Geographic range size 488 was estimated across the Neotropics and therefore would be less likely to be affected 489 by these exclusions. Regardless, the proportion of unidentified individuals was 490 relatively small (1% and 5% for 1-ha and small plots, respectively) and likely had only 491 a minor effect on our analyses. 492 We measured geographic range size as the extent of occurrence (EOO), 493 calculated as the area encompassed by the minimum convex polygon enclosing all 494 known occurrences of a species. This measure is affected by incomplete botanical 495 sampling, even though it may be relatively robust to this kind of sampling error (Joppa 496 et al., 2015). The effect of this error on estimates of phylogenetic signal depends on 497 how it is distributed across the phylogeny. Under the reasonable assumption that error 498 in measures of EOO is random relative to phylogenetic relationships, error in estimates 499 of EOO would introduce negative bias in estimates of phylogenetic signal (Housworth 500 et al., 2004). Thus, phylogenetic signal in geographic range size would be stronger 501 than we documented here. Deviations from the assumption above could introduce 502 negative or positive bias in estimates of phylogenetic signal, depending on the 503 particular relationship between measurement error and the phylogeny. 504 Phylogenetic patterns of rarity might depend on the spatial scale at which they 505 are examined (Mouillot & Gaston 2009; Machac et al., 2011; Krasnov et al., 2011). It 506 is therefore important to highlight that we measured habitat breadth and local 507 abundance at a regional scale (across the Madidi region), and geographic range size at 508 a continental scale (across the Neotropics). It is difficult to predict how our results 509 would change if habitat breadth and local abundance were measured at a continental 510 scale. Krasnov et al., (2011) suggested that phylogenetic patterns of habitat breadth 511 and abundance are stronger when measured at larger spatial extents. If, so, we would 512 expect that phylogenetic signal in habitat breadth and abundance measured at a 513 continental scale would be even higher than we documented here. 514 515 ACKNOWLEDGEMENTS 516 We are very grateful to Alfredo Fuentes and Leslie Cayola, who coordinate 517 most of the work in Bolivia, and to Alejandro Araujo-Murakami, Javier Quisbert, 518 Maritza Cornejo, Tatiana Miranda, Renate Seidel, Narel Paniagua, and Carla 519 Maldonado, who provided valuable plot data for this study. We also thank all the 520 taxonomic experts, students, and local guides involved in the collection of the field 521 data and in the identification of plant specimens. Peter Stevens provided valuable 522 advice for constructing phylogenies and conducting the analysis, as well as comments 523 on the manuscript. We also thank H. ter Steege, B. Enquist, B. McGill, M. Dornelas, 524 R. Field and an anonymous reviewer for helpful comments on the manuscript. We 525 thank Dirección General de Biodiversidad, the Bolivian Park Service (SERNAP), the 526 Madidi National Park and local communities for permits, access and collaboration in 527 Bolivia. This study was funded by the National Science Foundation (DEB 0101775, 528 DEB 0743457), the Missouri Botanical Garden, the National Geographic Society, the 529 Comunidad de Madrid (Spain), Universidad Autónoma de Madrid, and the Taylor and 530 Davidson families. 531 532 533 Appendix S1: Dated global family phylogeny source information 534 Appendix S2: Dataset source information 535 Appendix S3: Supplementary results 536 537 538 BIOSKETCH 539 M. Isabel Loza is a graduate student at the University of Missouri-Saint Louis. She is 540 part of the Madidi Project, a long-term collaborative project to document patterns in 541 plant diversity and distribution in the Bolivian Andes (www.mobot.org/madidi/). This 542 work was part of her MSc. dissertation. 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(2015) Niche breadth and geographic range size as 673 determinants of species survival on geological time scales. Global Ecology and 674 Biogeography , 24,1159-1169 675 48. Stevens, P. F. (2001 onwards). Angiosperm Phylogeny Website. Version 12, 676 July 2012 [and more or less continuously updated since]." 677 http://www.mobot.org/MOBOT/research/APweb/ . 678 49. Vuille, M. & Bradley, R. (2000) Mean annual temperature trends and their 679 vertical structure in the tropical Andes. Geophysical Research Letters, 27, 3885– 680 3888. 681 50. Waldron A. (2007) Null models of geographic range size evolution reaffirm its 682 heritability. The American Naturalist , 170, 221–231. 683 51. Webb, C. & Donoghue, M. J. (2005) Phylomatic, tree assembly for applied 684 phylogenetics. Molecular Ecology Notes, 5, 181–183. 685 52. Webb, C., Ackerly, D. & Kembel, S. (2011) Phylocom, Software for the analysis 686 of phylogenetic community structure and character evolution (with phylomatic 687 and ecovolve). pp. 38. 688 689 690 691 692 693 694 695 696 697 698 699 700 701 702 703 704 705 706 707 708 709 710 711 712 713 714 715 716 717 718 719 720 721 722 723 724 725 Figure 1. Geographic distribution of plots in the Madidi Region of Bolivia. The map 726 on the rigth shows the vegetation types according to Navarro & Maldonado (2002), 727 large plots (1.0 ha, triangles) and small plots (0.1 ha, circles). The map on the top right 728 shows the elevation gradient for Bolivia, the Madidi region in white and the plots in 729 red. 730 731 732 733 734 735 736 737 738 739 740 741 742 743 Local abundance Habitat breadth Geographic range size a h

0 . 1 Log Blomberg's K LogBlomberg's

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744 745 Figure 2. Distribution of Blomberg’s K values for observed data and the two null 746 models (Tip randomization and Brownian motion) using the ultrametric tree based on 747 the median ages for the large (1.0 ha) and small plots (0.1 ha). Boxplots show the 748 median (solid line), the interquartile range (box) and whiskers extending to the most 749 extreme values within 1.5 × interquartile ranges from the box. The distribution of 750 observed values was generated by repeatedly sub-sampling the species pool to obtain 751 one species per family. The horizontal lines at the bottom join observed and null model 752 distributions when they do not differ statistically (using adjusted p ≤ 0.05). 753 754 755 756 757 758 759 760 761 762 763 764 765 Local Abundance Habitat breadth Geographic range size

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766 767 Figure 3. Percent of variation in rarity axes explained by different taxonomic levels 768 according to a variance partitioning analysis for the large (1.0 ha) and small plots (0.1 769 ha). Large circles represent observed values and boxplots the distribution of values 770 generated by the tip randomization null model. Boxplots show the median (solid line), 771 the interquartile range (box) and whiskers extending to the most extreme values within 772 1.5 × interquartile ranges from the box. Dark and light gray backgrounds indicate that 773 observed values fall below or above the 95% confidence interval for the null model, 774 respectively. 775 776 777 778 779 780 781 782 783 784 785 786 787 788 789 790 791 Figure 4 . a. Phylogeny calibrated at the family level for large plots (1.0 ha), showing 792 which families are more common (blue dots) or rare (yellow dots) than expected by 793 chance. The closest ring to the phylogeny represents local abundance, the next habitat 794 breadth and the third geographic range size. b. Histogram showing the frequency of 795 node ages in the phylogeny in relationship with the evolutionary time. 796 797 798 799 800 801 802 803 804 805 806 807 808 809 810 811 812 813 814 Figure 5 . a. Phylogeny calibrated at the family level for small plots (0.1 ha), showing 815 which families are more common (blue dots) or rare (yellow dots) than expected by 816 chance. The closest ring to the phylogeny represents local abundance, the next habitat 817 breadth and the third geographic range size. b. Histogram showing the frequency of 818 node ages in the phylogeny in relationship with the evolutionary time. 819 820 821 822 823 824 825 826 827 828 829 830 831 832 833 834 e

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e 854 M 855 856 Figure 7. a. Phylogeny calibrated at the family level for the small plots (0.1 ha), 857 showing nodes with phylogenetic signal for geographic range size (grey squares) and 858 phylogenetic lability for abundance (black stars) according to disparity analysis. b. 859 Detail of the phylogeny focused on clades characterized by phylogenetic signal in 860 geographic range size and phylogenetic lability in abundance. 861 862