SNAKES OF THE PANTANAL: BIOGEOGRAPHY AND TAXONOMIC,
PHYLOGENETIC AND ECOMORPHOLOGICAL DIVERSITY
SERPENTES DO PANTANAL: DIVERSIDADE TAXONÔMICA, FILOGENÉTICA
E ECOMORFOLÓGICA
Liliana Piatti
SÃO PAULO
2017
UNIVERSIDADE DE SÃO PAULO INSTITUTO DE BIOCIÊNCIAS PROGRAMA DE PÓS-GRADUAÇÃO EM ECOLOGIA
Snakes of the Pantanal: biogeography and taxonomic, phylogenetic
and ecomorphological diversity
Serpentes do Pantanal: diversidade taxonômica, filogenética e
ecomorfológica
Aluno: Liliana Piatti
Orientador: Marcio Roberto Costa Martins
Co-orientadora: Vanda Lúcia Ferreira
SÃO PAULO
2017
1 RESUMO
2 A composição de comunidades biológicas atuais é resultado da interação da história
3 evolutiva dos organismos e dos ambientes com fatores locais contemporâneos que mediam
4 a ocorrência e coexistência das espécies. Em planícies de inundação como o Pantanal, os
5 pulsos de inundação são considerados as principais forças que mediam processos
6 ecológicos, que por sua vez controlam a distribuição espacial e temporal dos organismos e a
7 composição das comunidades. O Pantanal é a maior planície de inundação tropical e possui
8 uma biota menos rica que a encontrada em áreas de entorno. Isto tem sido atribuído as
9 condições adversas que os ciclos de inundações impõem sobre os organismos, e também à
10 recente formação da região. O principal objetivo desta tese foi investigar os padrões de
11 diversidade de comunidades de serpentes no Pantanal a respeito de suas origens,
12 apresentando e testando hipóteses sobre processos passados e atuais que operaram na
13 organização de comunidades de serpentes dessa planície inundável. Nós adotamos
14 abordagens que podem evidenciar processos em escalas temporais recentes e antigas, e
15 uma escala espacial ampla, que abrange toda a bacia hidrográfica onde o Pantanal está
16 situado – a bacia do Rio Paraguai. Nós encontramos que a fauna de serpentes do Pantanal é
17 parte de uma conjunto de espécies amplamente distribuído na bacia, que é relacionado à
18 calha do Rio Paraguai e às planícies associadas a ele. A bacia hidrográfica possui faunas
19 regionalizadas distribuídas ao redor da planície do Pantanal, a qual pode estar atuando
20 como barreira para algumas espécies e como corredor de dispersão para outras. Nossa
21 expectativa de que as inundações sazonais ajam como filtro ambiental, permitindo a
22 ocorrência na planícicie somente das espécies com adaptações para lidar com esses eventos
23 periódicos, não foi suportada. Ao invés disso, as inundações parecem diminuir a força
24 relativa dos processos determinísticos na organização das comunidades e então favorecem a
25 ocorrência de espécies com hábitos generalistas por causarem distúrbios recorrentes no
26 ecossistema. Filtros ambientais podem estar em ação por meio do gradiente de cobertura
1
27 de florestas, dando origem a comunidades mais ricas em áreas mais abertas e taxocenoses
28 formadas por espécies com uso de hábitat similares em áreas mais florestadas. Porém esses
29 padrões podem igualmente terem sido produzidos a partir das divergências ecológicas
30 observadas entre as biotas que se originaram em áreas abertas e florestadas da América do
31 Sul, e não pela ação isolada de um filtro ambiental.
32 ABSTRACT
33 Species composition in biological communities is a result of interactions of the evolutionary
34 history of both organisms and environments, along with local factors that currently mediate
35 species occurrence and coexistence. In floodplains, like the Pantanal, flood pulses are
36 recognized as the main driver of ecological processes that control both species spatial and
37 temporal distribution, but also shape communities. The Pantanal is the largest tropical
38 floodplain on Earth and it has a less rich biota than that of surrounding regions. This has
39 been attributed to the hardness imposed by the flood cycles on the organisms and also to
40 the recent formation of the plain. The main goal of this thesis was to investigate diversity
41 patterns of the snake community of the Pantanal regarding their origins, through stating and
42 testing hypothesis about past and present processes that acted on the current assembly of
43 snake communities in this seasonal floodplain. We adopted approaches that provided
44 evidences for processes at deep and recent time scales, as well as a wide special scale, that
45 encompasses the entire hydrographic basin where the Pantanal is located – the Paraguay
46 River Basin. We found that Pantanal snake fauna belongs to a species group widely
47 distributed in the basin, and is linked to the Paraguay River channel and nearby lowland
48 areas. The entire basin has regionalized faunas distributed around the Pantanal floodplain,
49 which may be acting as a barrier for some species and as a dispersal corridor for others. Our
50 expectation that seasonal flooding could act as an environmental filter, allowing only species
51 with adaptations to deal with this recurrent event to occur, was not supported. Rather than
2
52 that, flooding seemed to be decreasing the relative force of deterministic processes on
53 community assembly and so favoring species with generalist habits by promoting recurrent
54 ecosystem disturbances. Environmental filter can be acting through the forest cover
55 gradient, giving origin to richer communities in more open areas and assemblages formed by
56 species with similar habitat uses in more forested aeras. However, these patterns also could
57 have originated from the ecological divergences between biotas originating from open and
58 forested areas in South America.
59 GENERAL INTRODUCTION
60 The Pantanal, the largest wetland on Earth, is located in central South America [1,
61 2]. The physical and biological aspects of this floodplain seem strongly related to its annual
62 cycles of floods and droughts: flooding provides a permanent exchange of water, sediments,
63 chemical components, and organisms between the main river channels and adjacent areas
64 [1]; the vegetation is distributed over a flooding gradient, in accordance to tolerance to
65 either flood or drought [3]; and animal communities experience shrinking and expansion of
66 habitats related to the flooding pulse and adapt their natural history or behavior to it [4].
67 Because the Pantanal is a converging point of several South American ecoregions [2],
68 representatives from other biotas are found in the floodplain along with species that have
69 wide distributions, and often they establish large populations in the floodplain [5].
70 Notwithstanding, in general the Pantanal biota is less rich than that of surrounding regions,
71 and thus far no endemisms have been confirmed in the floodplain. This lack of endemicity
72 and decreased richness have been attributed to the harshness imposed by the seasonal
73 cycles on the organisms and also to the recent formation of the Pantanal [2, 3, 4, 5].
74 The main phase of subsidence that resulted in the wetland depression occurred
75 during the transition between the Pliocene and Pleistocene, about 2.5 million years ago [6].
76 The vast plain that resulted from this event nowadays stores water originating from the
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77 surrounding upland regions, and delivers it slowly to the lower sections of the basin. Due to
78 the very low declivity of the terrain, during the rainy season water overflows the main river
79 channels and flows slowly from east to west in permanent and temporary streams and then
80 southward upon entering the Paraguay River [1, 7, 8]. Seasonal fluctuations in water level
81 generally range from 2 to 5 m in the Paraguay River, but typically have lower values across
82 the Pantanal plains, with flooding taking from 3 to 6 months to move across the whole
83 floodplain [1, 7, 8].
84 In floodplains like the Pantanal, the flood pulses drive important seasonal ecosystem
85 changes [9, 10], activating ecological processes that control both the spatial and temporal
86 distribution of organisms as well as their life-history strategies. River flow regime
87 adaptations range from behaviors that result in the avoidance of individual floods or
88 droughts, to morphological changes and life cycles that are synchronized with long-term
89 flood patterns [11, 12, 13]. The adverse effects from flooding are responsible for changes in
90 distribution and species composition for several taxa in many regions [4, 11, 14, 15, 16, 17].
91 Researchers have argued that stressful environments, such as seasonally flooded areas, can
92 act as environmental filters [11, 14, 18]. When this occurs, biological communities of these
93 areas are composed only of organisms exhibiting adaptations to deal with the stressful
94 conditions found there, and species in the regional pool that are not adapted to area’s
95 conditions are excluded.
96 Environmental filters are one of the processes that assemble biological
97 communities. In addition to them, species interactions, such as competition, also play a role
98 in structuring of local assemblages [19]. The resultant patterns of filter process are
99 communities composed by more similar species than expected considering the regionally
100 available species pool, because these organisms have the same traits needed for
101 maintaining viable populations in the habitat where the community was established [20]. On
102 the other hand, when interispecific competition is the main force in the assembling of
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103 communities, these will be composed by species with divergent traits, which allows them to
104 use available resources in different ways and co-occur in areas with limited resources [20].
105 Additionally to these deterministic forces, community assembly can be simultaneously
106 influenced by factors that are relatively more stochastic, which include unpredictable
107 disturbance, probabilistic dispersal and random birth-death events [21, 22]. These events act
108 equally on organisms despite their taxonomic identity and result in local communities that
109 are a random subsets of the regional species pool present on larger spatial scales [23].
110 In turn, the diversity patterns observed in regional species pools often are more
111 influenced by historic process that occur at larges scales of time and space, such as species
112 immigration, speciation, and extinction [24]. By the action of theses forces it is possible that
113 regional communities may only contain a subset of the diversity from the areas of origin, or
114 may have diversified with particular ecological tendencies, which constrain the range of
115 possible outcomes that local processes could produce [25]. So, species distributions are
116 shaped by the interplay between evolutionary and ecological processes and one of the
117 major challenges in ecology remains in identifying the processes that regulate species
118 composition in different communities and their relative forces [21, 22, 24].
119 The knowledge about current snakes communities in the Neotropics highlights the
120 strong influence of historical process, such as origin and dispersion of particular clades, on
121 the composition of local assemblages [26, 27, 28]. The three main South American snake
122 lineages have distinct geographic distribution patterns, in consequence, communities from
123 different locations have divergent patterns of species dominance and resource use [26]. But
124 the action of current ecological processes, such as environmental filters, on the diversity of
125 Brazilian snake communities also was evidenced recently: communities from open areas
126 tend to be more clustered than those from forested areas because open areas constrains
127 the occurrence of species with arboreal habitats [29]. Information on snakes of the Pantanal
128 floodplain are yet scarce. Similarly to other organisms, the snake fauna is mixture of the
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129 elements from the surrounding ecoregions, with lower species richness and no endemism
130 [30, 31, 32]. Local community composition follows the expected dominance of Dipsadidae
131 species due to the history of Neotropical snake clades, and is more similar to open domains,
132 such as the Cerrado and the Chaco, than to neighboring forested areas [30, 31, 32]. Despite
133 the hypothesis of environmental similarity with open ecoregions and restrictions imposed by
134 flooding have benn often indicated as primary causers of the recorded diversity patterns,
135 the processes involved in the assembly of Pantanal snake communities, locallly and
136 regionally, have not yet been formally addressed.
137 By characterizing phylogenetic relationships among species within a particular
138 community and among communities, in relation to the regional pool and along with analyses
139 of functional diversity of the assemblages, it is possible to detect the ecological processes
140 that were important in creating the current structure observed in local communities, which
141 species traits these forces act on, and through which, if any, environmental feature they are
142 operating [19, 20, 21, 22]. And through the study of the regional distribution of species
143 associated with particular areas and biogeographical events, it is possible to investigate
144 processes on larger spatial and temporal scales, generating hypotheses about the origin and
145 dispersion of the biota in a particular region [33, 34].
146 In this context, the main goal of this dissertation was to investigate the patterns of
147 diversity of snakes in the Pantanal wetland regarding their origins, presenting and testing
148 hypothesis about past and present processes that acted on the assemblage of current
149 communities of this seasonal floodplain. Considering that the structure of biological
150 communities can be seen as an aggregate property of phenomena on different scales of
151 time and space [24, 35] we adopted approaches that can evidence processes acting on deep
152 and recent time scales, and a wide spacial scale, that encompasses the entire Paraguay River
153 Basin the hydrographic basin where the Pantanal is located. This work is divided in three
154 chapters in the format of scientific papers following the style and organization indicated by
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155 the journal PLOS One. We dedicated the first chapter to a more historical examination,
156 trying to discover whether the snake fauna that occurs in and around the Pantanal
157 floodplain has divergent biogeographical origins and whether the rise of the Pantanal
158 affected the ancestral biota of the region. In the second chapter we searched for
159 environmental and historical factors that drove the turnover of species between different
160 communities inside and outside the Pantanal, in the Paraguay River Basin. And in the third
161 chapter we analyzed the functional and phenotypical structure of these communities to
162 investigate if flooding or other environmental gradients are acting as environmental filters
163 for snakes, or if other processes are more important in the assembly of local communities in
164 the Pantanal and in the Paraguay River Basin.
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184 8. Hamilton SK. Hydrological controls of ecological structure and function in the 185 Pantanal Wetland (Brazil). In: McClain ME, editor. The ecohydrology of South 186 American rivers and wetlands. Wallingford: IAHS Press; 2002. pp.133-158. 187 9. Ward JV. Riverine landscapes: biodiversity patterns, disturbance regimes, and 188 aquatic conservation. Biol Conserv. 1998; 83:269-278. 189 10. Junk WJ, Wantzen KM. The flood pulse concept: new aspects, approaches, and 190 applications—an update. In: Welcomme RL, Petr T, editors. Proceedings of the 191 Second International Symposium on the Management of Large Rivers for Fisheries, 192 Volume 2. Food and Agriculture Organization & Mekong River Commission. RAP 193 Publication 2004/16. Bangkok: FAO Regional Office for Asia and the Pacific; 2004. 194 pp. 117-149. 195 11. Nicole KM, Clements WH, Guevara LS, Jacobs BF. Resistance and resilience of stream 196 insect communities to repeated hydrologic disturbances after a wildfire. Freshw 197 Biol. 2004; 49:1243-1259. 198 12. Renofalt BM, Nilsson C, Jansson R. Spatial and temporal patterns of species richness 199 in a riparian landscape. J Biogeogr. 2005; 32:2025-2037. 200 13. Lucas CM, Sheikh P, Gagnon PR, McGrath DG. How livestock and flooding mediate 201 the ecological integrity of working forests in Amazon River floodplains. Ecol Appl. 202 2016; 26:190-202. 203 14. Lytle DA, Poff NL. Adaptation to natural flow regimes. Trends Ecol Evol, 2004; 16:94- 204 100. 205 15. Parolin P, De Simone O, Haase K, Waldhoff D, Rottenberger S, Kuhn U, et al. Central 206 Amazon floodplain forests: tree survival in a pulsing system. Bot Rev. 2004; 70:357- 207 380. 208 16. Ferreira CS, Piedade MTF, Junk WJ, Parolin P. Floodplain and upland populations of 209 Amazonian Himatanthus sucuuba: effects of flooding on germination, seedling 210 growth and mortality. Environ Exp Bot. 2007; 60:477-483. 211 17. Gerisch M, Agostinelli V, Henle K, Dziock F. More species, but all do the same: 212 contrasting effects of flood disturbance on ground beetle functional and species 213 diversity. Oikos. 2012; 121:508-515. 214 18. Poff NL. Landscape filters and species traits: Towards mechanistic understanding 215 and prediction in stream ecology. J North Am Bentholog Soc. 1997; 16:391-409. 216 19. Webb CO, Ackerly DD, McPeek MA, Donoghue MJ. Phylogenies and Community 217 Ecology. Annu Rev Ecol Syst. 2002; 33:475-505.
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218 20. Weiher E, Keddy P. Ecological Assembly Rules. Perspectives, Advances, Retreats. 219 Cambridge : Cambridge University Press; 1999. 220 21. Leibold MA, Holyoak M, Mouquet N, Amarasekare P, Chase JM, Hoopes MF et al. 221 The metacommunity concept: a framework for multi-scale community ecology. Ecol 222 Lett. 2004; 7:601-13. 223 22. Kembel SW. Disentangling niche and neutral influences on community assembly: 224 assessing the performance of community phylogenetic structure tests. Ecol Lett. 225 2009; 12:949-960. 226 23. Hubbell SP. The unified neutral theory of biodiversity and biogeography. Princeton: 227 Princeton University Press; 2001. 228 24. Vellend M. Conceptual synthesis in community ecology. Quat Rev Biol 2010; 85:183- 229 206. 230 25. Fukami T. Historical contingency in community assembly: integrating niches, species 231 pools, and priority effects. An Rev Ecol Evol Syst. 2015; 46: 1-23. 232 26. Cadle JE, Greene HW. Phylogenetic patterns, biogeography, and the ecological 233 structure of neotropical snake assemblages. In: Ricklefs RE, Schluter D, editors. 234 Species Diversity in Ecological Communities: historical and geographical 235 perspectives. Chicago: University of Chicago Press;1993. pp.281-293. 236 27. França FGR, Mesquita DO, Nogueira CC, Araújo AFB. Phylogeny and ecology 237 determine morphological structure in a snake community in the central Brazilian 238 Cerrado. Copeia. 2008; 1:23-38. 239 28. Burbrink FT, Myers EA. Both traits and phylogenetic history influence community 240 structure in snakes over steep environmental gradients. Ecography. 2015; 38:1036- 241 1048. 242 29. Cavalheri H, Both C, Martins M. The interplay between environmental filtering and 243 spatial processes in structuring communities: the case of Neotropical snake 244 communities. PLoS ONE. 2015; 10(6):e0127959. 245 30. Strüssmann C, Prado CPA, Ferreira VL, Kawashita-Ribeiro R. Diversity, ecology, 246 management and conservation of amphibians and reptiles of the Brazilian Pantanal: 247 a review. In: Junk Wj, Da Silva CN, Wantzen KM, editors. The Pantanal: Ecology, 248 biodiversity ad sustainable management of a large Neotropical seasonal wetland. 249 Moscow: Pensoft Publishers; 2011. pp.497-521.
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250 31. Terra JS. Influência de fatores ambientais e espaciais nas comunidades de anfíbios e 251 répteis da Fazenda Nhumirim, Pantanal da Nhecolândia, MS. MSc thesis, 252 Universidade Federal de Mato Grosso do Sul. 2012. 253 32. Silva Junior MF. Serpentes da fazenda Nhumirim, Pantanal da Nhecolândia, Mato 254 Grosso do Sul, Brasil: composição e comparações com outras taxocenoses 255 Sulamericanas. Msc thesis; Universidade Federal de Mato Grosso. 256 33. Humphries CJ, Parenti LR. Cladistic biogeography: Interpreting Patterns of Plant and 257 Animal Distributions. Oxford: Oxford University; 1999. 258 34. Wiens JJ. The niche, biogeography and species interactions. Philos Trans R Soc Lond 259 B Biol Sci. 2011; 366:2336-2350. 260 35. Wiens JJ, Donoghue MJ. Historical biogeography, ecology and species richness. 261 Trends Ecol Evol. 2004; 19:639–644.
10
CHAPTER 1
The Role of the Pantanal Floodplain in the Biogeographical Patterns of
Snakes in the Paraguay River Basin, Central South America
Jacqueline Pimentel Silva1 ¶, Liliana Piatti2¶, Christine Strüssmann3, Vanda Lúcia Ferreira4,
Cristiano de C. Nogueira 5 and Marcio Martins 5*
¹Programa de Pós-Graduação em Ecologia e Conservação da Biodiversidade, Instituto de
Biociências, Universidade Federal de Mato Grosso, Cuiabá, Mato Grosso, Brazil
²Programa de Pós-Graduação em Ecologia, Instituto de Biociências, Universidade de São
Paulo, São Paulo, São Paulo, Brazil
3Faculdade de Medicina Veterinária, Universidade Federal de Mato Grosso, Cuiabá, Mato
Grosso, Brazil
4Centro de Ciências Biológicas e da Saúde, Universidade Federal de Mato Grosso do Sul,
Campo Grande, Mato Grosso do Sul, Brazil
5Departamento de Ecologia, Instituto de Biociências, Universidade de São Paulo, São Paulo,
São Paulo, Brazil
*Corresponding author
Email: [email protected] (MM)
¶ These authors contributed equally to this work
Short title: Biogeography of the Paraguay River Basin snakes
11
1 ABSTRACT
2 Topographical gradients caused by elevation and subsidence of landscapes are reflected in
3 diversity patterns of several taxa in the Neotropical region, that harbors one of the richest
4 snake faunas of the world. The Pantanal floodplain is the largest continuous tropical wetland
5 area, which arose during last phase of Andean orogeny and changed the primitive drainages
6 of different river basins. The whole floodplain and surrounding areas have never been the
7 main focus of objective analyses of raw distributions under a historical biogeographical
8 perspective. Our aim here was to propose a vicariance model for the Paraguay River basin
9 (PRB) and to test whether snake distribution patterns are consistent with the hypothesis of
10 the Pantanal floodplain as biogeographical barrier. We gathered and reviewed
11 georeferenced snake records from across PRB to test for non-random clusters of species
12 ranges (biotic elements). We used chi-square tests and phylogenetic dispersion indices to
13 test if closely related species were scattered among the different biogeographical units. We
14 subsequently examined spatial relationships between the recovered units and the Pantanal
15 and other putative geographical barriers.Results showed four non-random clusters of
16 species range (biotic elements), comprising 149 species. Phylogenetically close species
17 belonged to different biotic elements. Biotic elements were associated with geographical
18 (Paraguay River, Pantanal floodplain) and ecological barriers. All species from the Pantanal
19 floodplain belonged to a widely distributed biotic element or to the noise component. The
20 limits of the other three biotic elements did not coincide with the borders of the Pantanal,
21 but were restricted to marginal plateaus. Thus, we found a strong regional pattern in the
22 snake fauna of the PRB. The biogeographical pattern found seems influenced by the
23 Pantanal floodplain; it forms the core of a widespread cluster of lowland snake ranges, and
24 may act as a barrier to more restricted biotic elements in surrounding plateaus.
12
25 INTRODUCTION
26 Knowledge of the origins of South American biotas has increased in recent decades
27 as a result of biogeographical studies covering diverse landscapes and several groups of
28 animals and plants (e.g., [1-7]). Vicariance is recognized as an important mechanism driving
29 diversification [8, 9] and Neotropical biodiversity is influenced by this process [10-16].
30 However, few biogeographical studies in the Neotropics have tested the presence of non-
31 random clusters of species ranges, a central prediction of the vicariance model and a crucial
32 step for the delineation of biogeographical units [17].
33 Recurrence of vicariance events over time and space promotes the emergence of
34 distinct and regionalized biotas, by way of fragmentation of ancestral ranges through the
35 appearance of barriers [8, 9, 18]. Thus, according to the vicariance model, the best
36 explanation for non-overlapping distributions between sister groups is the fragmentation of
37 widespread ancestral ranges caused by emerging barriers, followed by allopatric speciation
38 [8, 18]. On average, species ranges originated at the same side of a vicariant barrier will tend
39 to overlap and be more similar to each other than to ranges originated at the other side of
40 the barrier [9, 18, 19]. Thus, the vicariance model predicts a non-random congruence of
41 species ranges, with species originating in a particular area forming a biotic element, that is,
42 a group of species whose ranges are more similar to each other than to those from other
43 groups [19]. Moreover, if speciation is the result of vicariant events, closely related species
44 should originate on different sides of a barrier and, hence, should belong to different biotic
45 elements [19].
46 The Pantanal floodplain is the largest continuous tropical wetland area. It is located
47 at the center of South America, in the depression of the Paraguay River basin. It arose during
48 the Pliocene/Pleistocene transition (about 2.5 ma), in the last phase of Andean orogeny
49 after the subsidence and changes in primitive drainages of the Paraná, Paraguay and Tapajós
50 river basins [20, 21, 22, 23]. The Pantanal floodplain is surrounded by ancient uplands of pre-
13
51 Cambrian, Paleozoic and Mesozoic origins. These uplands make up different ecoregions,
52 namely the southwestern Amazon moist and seasonal forests, Cerrado savannas, Chiquitano
53 dry forest, and the dry and humid Chaco [24, 25]. The Paraguay basin is, as a result, one of
54 the most ecologically and geologically heterogeneous areas of South America [26].
55 Our understanding of the historical relationship between the Pantanal and
56 surrounding biotas is limited and controversial [13, 27, 28, 29]. The whole floodplain and
57 surrounding areas have never been the main focus of objective analyses of raw distributions
58 under a historical biogeographical perspective. In floodplains, strong seasonal cycles of
59 floods and droughts determine species diversity and composition [30] and, in general, the
60 Pantanal has a poorer biota than the surrounding regions, possibly because of its more
61 recent formation [31]. Recent studies have suggested a vicariant event for fishes, with the
62 Pantanal subsidence disrupting the ranges of rheophilic taxa once widely distributed
63 throughout Central-Brazilian river basins [32]. In the case of birds, closely related pairs or
64 triads of species occur in allopatry at the edges of the Pantanal, and are absent from the
65 floodplain [27, 33]. It has been suggested that the subsidence of the Chaco plain and
66 adjacent areas was the vicariant event that split these populations into their current disjunct
67 ranges [10]. A biogeographical analysis of Cerrado squamate reptiles has also shown that the
68 species composition in the Pantanal depression differs from that from neighbouring plateaus
69 [12]. The diversity patterns have evidently been significantly influenced by topographical
70 gradients, the faunas in currently elevated plateaus being more isolated compared to those
71 in the depressions, where biotic interchange is more intense [12].
72 Snakes are notable for their high diversity and occurrence in a variety of
73 environments. This is generally attributed to their high speciation rates in tropical areas and
74 great adaptability to available resources [34, 35]. Some morphological adaptations of body
75 shape driven by habitat use seem to deeply affect various ecological attributes of species,
76 such as diet and the ability to use a wide array of available microhabitats, which range from
14
77 fossorial, semifossorial, aquatic, terrestrial and arboricolous [34, 36]. Furthermore, snakes
78 have relatively lower dispersal abilities and smaller ranges than mammals and birds [37],
79 which could strengthen their relations with smaller scale environmental conditions and
80 favor the detection of vicariant co-distributions.
81 Here, we propose a vicariance model for the Paraguay River basin by assessing
82 whether the origins of the Pantanal floodplain may have promoted vicariant events for
83 ancestral taxa. Our main goals are: (1) to search for non-random clusters (biotic elements,
84 sensu [19]) of snake ranges in the Paraguay River basin; (2) to test whether closely related
85 species belong to different biotic elements (see [38]); and (3) assess whether the spatial
86 configuration of biotic elements is consistent with the hypothesis that the origin of Pantanal
87 promoted a vicariant event that isolated ancestral snake ranges.
88 MATERIALS AND METHODS
89 Study area
90 The Paraguay River basin (PRB) is located between 14° and 27° S and 53° and 67° W
91 (map in S1 Appendix). The entire catchment area covers 1,135,000 km2, and includes almost
92 all of Paraguay and parts of Bolivia, Brazil and Argentina. The basin includes a number of
93 terrestrial ecoregions (sensu [24]): Cerrado, Chiquitano dry forest, Bolivian montane dry
94 forest, dry Chaco, humid Chaco, Alto Paraná Atlantic forest, Central Andean Puna, Southern
95 Andean Yungas and the Pantanal (map in S1 Appendix). The Pantanal is a floodplain covering
96 an area of about 140,000 km2 situated in the upper Paraguay River depression (map in S1
97 Appendix). The area is subjected to an annual, predictable, monomodal flood pulse. During
98 the rainy season (November–March), the vast plain stores water and delivers it slowly to the
99 lower sections of the Paraguay River during the dry season (April–October). Flood intensity
100 varies, but, on average, about one-third of the Pantanal fills up each year, with monthly
101 estimates of total flooded area ranging from 10 to 70% of the entire Pantanal depression
15
102 [39]. Because of slight declivity of the terrain (2 to 3 cm per km from north to south, and 5 to
103 25 cm from east to west) floodwaters take about four months to run through the entire
104 Pantanal [39]. The vegetation is a mixture of plant communities from the surrounding
105 biomes: moist forests from the Amazon basin and the Atlantic forest, Cerrado savannas from
106 central Brazil, and dry and wet Chacoan savannas from Bolivia and Paraguay [40]. The limits
107 of the Paraguay basin and the Pantanal adopted here follow [41] and [39], respectively.
108 Species distribution data
109 We obtained species distribution data from 6562 georeferenced snake records from
110 localities across the Paraguay basin (map in S1 Appendix). Records were gathered either by
111 examination of voucher specimens in 12 Brazilian zoological collections (about 60% of the
112 records; see map in S1 Appendix) or by the compilation of reliable literature records (around
113 25%). The database was completed (another 15%) with unpublished data obtained from
114 management plans of protected areas, unpublished technical reports of environmental
115 impact studies, and with original data from partner researchers, whenever they could be
116 confirmed through examination of voucher material. There were no requirements from the
117 Brazilian government for previous submissions of our research to ethics committee, as our
118 methodology did not include the collection of living specimens.
119 Geographical coordinates were obtained by contacting original collectors whenever
120 possible. Alternatively, they were obtained after visual inspection in Google Earth 7.1. If
121 detailed information on localities were lacking, we used municipality centroids. All species
122 that had at least one record available in the Paraguay basin were included in our analysis.
123 Taxonomy followed [42], [43], [44] and [45].
124 Vicariance model and phylogenetic relationships
125 To propose a vicariance scenario for the Paraguay River basin snake fauna, we used
126 biotic element analysis [17], which tests two central predictions of the vicariance model: a)
127 the presence of non-random clusters of ranges (named biotic elements), and b) whether
16
128 closely related species are randomly scattered in different biotic elements. Biotic element
129 analysis looks for predictable patterns produced by vicariance, using raw distribution data
130 alone, and without requiring strict allopatry of species ranges. Thus, this analysis is more
131 robust than traditional area-of-endemism approaches, which are highly affected by dispersal
132 [17, 19, 46].
133 The central idea behind biotic element analysis is that ranges resulting from
134 vicariant events will form clusters, or groups, of geographically structured ranges, which are
135 more similar and less distant to each other than to the ranges of other clusters [17]. Thus,
136 species ranges restricted to the same side of a given barrier may not be identical, or fully
137 sympatric, but will tend to be more similar than ranges formed on the other side of the
138 barrier (see [17]). Biotic element analysis starts from raw point localities mapped onto areas
139 (usually grid cells), resulting in a presence-absence matrix of species in areas. The procedure
140 first computes distances between ranges, varying from 1 (no range overlap, i.e. no shared
141 grid cells) to 0, in fully co-occurring and equal-sized ranges (all presences in grid cells shared
142 by the two species). Then the analysis computes an overall distance measure (T value, [17])
143 between observed ranges [17]. Using Monte Carlo simulations, the program then produces
144 the approximate distribution of T values in sets of simulated ranges, under a null model
145 produced in accordance with the original range sizes, the number of taxa per geographic
146 unit, and the spatial autocorrelation of the occurrences of a taxon [17, 47]. Finally, the
147 observed T statistic is compared to values obtained in the null model. If the observed T value
148 falls within the 95% confidence interval of simulated T values, then no significant range
149 clustering is observed, and the first prediction of the vicariance model is not corroborated.
150 In the case of non-random clustering of species ranges, the observed T ratio is expected to
151 be lower than T ratios obtained by simulations, as clustered species ranges tend to show
152 smaller distance values than random ranges [17, 47].
17
153 In order to produce a presence-absence matrix of species across the study area, we
154 divided the PRB in 89 1x1 degree grid cells. The grid size was chosen seeking to minimize
155 sampling gaps (cells with no occurrence), without losing the details of the occurrence
156 records. We used the “geco” coefficient [47] to calculate the distance matrix between
157 species ranges. This coefficient is derived from the Kulczynski index (as described in [17]),
158 but takes into account the geographical distances between occurrences of the taxa, and is
159 considered robust against the pervasive problem of incomplete sampling [47]. To compare
160 real data with null models we used 1000 randomizations. Analyses were implemented using
161 the “prabclus” package [17] in R statistical software [48].
162 In the case of significant range clustering, biotic elements were then determined by
163 inferring the number of meaningful clusters of species that had similar ranges. For this we
164 used the Model Based Gaussian Clustering (MBGC) as implemented in the package “mclust”
165 [49]. In contrast to other clustering methods, MBGC decides on the number of meaningful
166 clusters and the number of ranges that cannot be assigned adequately to any cluster—the
167 noise components [17, 50]. MBGC operates on a metric scale data set, therefore it is
168 necessary to perform a multidimensional scaling on the matrix of range distances. This step
169 also requires an initial estimation of noise, and the number of dimensions required in
170 multidimensional scaling. We used four multidimensional scaling dimensions and, as
171 suggested by [17], divided the number of species by 40 for detecting the initial noise
172 component. In addition to defining the ranges of biotic elements, we mapped the areas with
173 the highest richness as the core areas of each biotic element—cells with 70% or more of the
174 species recorded in a given biotic element [12]. Given that dispersal may blur the limits of
175 biotic elements, the definition of core areas improves spatial visualization, reducing overlap
176 between adjacent units, and highlighting the segregation among range clusters.
177 After biotic elements are determined, the second prediction of the vicariance model
178 can be tested. It expects a uniform distribution of closely related species across biotic
18
179 elements, as a result of allopatric speciation. In a vicariance scenario, phylogenetically close
180 species should be found in different biotic elements, and not gathered within the same
181 range clusters. This corresponds to the cross-table expected under the null hypothesis of
182 independence of rows and columns [17]. We used chi-square statistic to test for
183 independence of rows and columns of the cross-table, with species classified according to
184 their phylogenetic relationships (in rows) and presence in biotic elements (columns). We
185 pruned the phylogenetic hypothesis of squamate reptile proposed by Tonini et al. 2016 [51]
186 to assemble a phylogeny of the snakes in Paraguay River basin. This pruned tree includes
187 70% of the described species recorded in the area (see details in S2 Appendix).
188 Phylogenetically close species were those that appeared as sister species in this phylogeny.
189 We excluded from this step of analysis noise species (species not present in the biotic
190 elements) and species in polytomies. However, if after removing noise species from a
191 polytomy a pair of species remained, theses were not excluded. A total of 34 species pairs
192 were included in this analysis.
193 We also used the Net Relatedness Index (NRI) and Nearest Taxon Index (NTI, see
194 [52]) to further test if the distribution of snakes among biotic elements was random in
195 respect to phylogeny, i.e., whether closely related species were scattered among different
196 biotic elements (BEs), or clustered within particular BEs, using the “picante” package [53].
197 Under a vicariance scenario, biotic elements should be formed by species randomly
198 dispersed or overdispersed in the phylogeny, but not clustered into particular clades. To
199 calculate the significance of these indices we built null models by randomizing
200 presence/absences and keeping the original richness of each biotic element. Negative NRI
201 and NTI values indicate overdispersion and positive values, clustering [53].
202 To avoid bias in our results caused by missing species in the phylogeny we repeated
203 the chi-squared test and the NRI and NTI indexes using a phylogenetic hypothesis
204 encompassing all PRB snakes species. We used Mesquite 3.1 [54] to assemble by hand a
19
205 composite phylogeny (see, e.g., [55, 56]) based primarily on Grazziotin et al. (2012) [43],
206 Pyron et al. 2013 [57] and Tonini et al. 2016 [51], and then collating information from
207 various additional phylogenies (see details in S2 Appendix). The placement of species that
208 were not included in the published phylogenies was inferred according to the relationships
209 of sister species or included as a polytomy in nodes containing its closely related species.
210 This composite phylogeny has no branch lengths because the sources differ in methodology.
211 A total of 39 species pairs were included in the chi-square test using this composite
212 phylogeny. In order to test the effect of polytomies in our result based on this tree (see
213 [52]), we used the “ape” package [58] to randomly resolve all polytomies in our tree 999
214 times. For each of these simulated trees, we calculated NRI and NTI again and compared
215 them to our original results.
216 RESULTS
217 We recorded 161 snake species in nine families in the Paraguay River basin (S1
218 Table). In the Pantanal floodplain we recorded 84 species, with no confirmed endemism (S1
219 Table). Species ranges in the basin were significantly non-random, in agreement with the
220 first prediction of the vicariance model: observed T = 0.397 was significantly smaller (p <
221 0.001) than T values in null models, which varied between 0.401 and 0.478 (average 0.430).
222 The cluster analysis detected four biotic elements, formed by 149 species, with 12
223 species detected as noise components. Biotic element 1 (BE1) was formed by 106 species (c.
224 65% of the total number of species recorded). Although most species in this group were
225 widespread, core areas of BE1 were located in portions of the central Brazilian plateau (Fig
226 1), surrounding the Pantanal floodplain, in areas ranging between 450 and 1000 m above
227 sea level (namely, the Guimarães, Bodoquena, Maracajú-Campo Grande and Urucum
228 plateous). Biotic element 2 (BE2) grouped 16 species, limited mostly to uplands in the
229 northern edges of the Paraguay basin (Fig 1), with core areas in peripheral plateous between
20
230
231 Fig 1. Biotic elements of the Paraguay River basin (PRB). Dark grey cells have more than 232 70% of species from that BE (BE core areas); light grey cells have more than 30% of the 233 species, and empty cells have less than 30% of the species of a given BE. In the 234 background, different shades of grey indicate elevation above sea level. The Paraguay 235 River basin is delimited by the black line, and the Pantanal floodplain by the checkerboard 236 pattern.
237 600 and 1000 m above sea level (Província Serrana mountain range, Guimarães plateau and
238 southern limits of the Parecis plateau). Biotic element 3 (BE3) was formed by 15 species
21
239 distributed in south-eastern portions of the Paraguay basin, restricted to the eastern side of
240 the Paraguay river (Fig 1). Core areas of BE3 were located in Paraguayan plateaus, ranging
241 between 500 and 800 m above sea level (Cordillera de San Rafael, Cordillera de Altos, and
242 Serranía San Joaquin). Finally, biotic element 4 (BE4) was formed by 12 species distributed
243 along the western part of the Paraguay River basin with a single core area in the Chaco plain
244 in Paraguay and disjunct areas of intermediate richness in southern Bolivia, northern
245 Argentina and western Paraguay (Fig 1). No biotic element was totally congruent with the
246 limits of the Pantanal floodplain (Fig 1). All species recorded for the floodplain were
247 attributed to the widespread BE1 (94%) or to the noise component (five species; see S1
248 Table). The other three biotic elements (BE2-4) did not overlap with the Pantanal, but
249 instead are found mostly in adjacent, upland areas.
250 The chi-square tests indicated that phylogenetically close species are randomly
251 scattered among different biotic elements in both pruned and composite phylogeny (p =
252 0.523 and p = 0.97, respectively). Thus, our results also corroborate the second prediction of
253 the vicariance model, with phylogenetically close species forming different biotic elements.
254 This same pattern was observed in most of cases when BE composition was assessed using
255 recently proposed metrics of phylogenetic clustering (NRI and NTI): all Bes, using the both
256 phylogenies, are randomly arranged, formed by different, random clades, according to the
257 NRI metric (Table 1, S2 Appendix). According to the NTI index, which only considers the
258 relation between each species and its closest relative, BE3 is phylogenetically clustered in
259 relation to the composite phylogeny (see Table 1; see also S2 Appendix), being thus formed
260 by fewer lineages than expected. Results were the same when we tested for an effect of
261 polytomies in the composite phylogeny. All simulated trees with randomly resolved
262 polytomies were random with respect to phylogenetic relationships considering NRI index
263 and showed BE3 clustered for NTI.
22
264 Table 1. Values of Net Relatedness Index (NRI) and Nearest Taxon Index (NTI) for each 265 biotic element (BE) of snakes from the Paraguay River basin, including their significance 266 values compared to values in 1000 random assemblages. BE NRIp NTIp NRIc NTIc
BE1 0.400 -0.769 0.932 1.063
BE2 -1.032 -1.073 -0.649 0.524
BE3 0.070 1.519 0.059 2.033*
BE4 0.271 1.021 0.382 0.015
267 BE, Biotic elements; NRIp, Net Relatedness Index using pruned phylogeny (see text); NTIp, 268 Nearest Taxon Index using pruned phylogeny; NRIc, Net Relatedness Index using composite 269 phylogeny; NTIc, Nearest Taxon Index using composite phylogeny; negative NRI and NTI 270 values indicate overdispersion and positive values clustering. *p < 0.05
271 DISCUSSION
272 Our results reveal a significant clustering of ranges in the snake fauna of the
273 Paraguay River basin. Groups of species with similar ranges, formed by phylogenetically
274 random arrays of species, are restricted to particular portions of the basin. Location of the
275 restricted BEs 2 to 4 coincide with prominent environmental features of the Paraguay basin,
276 including isolated upland areas of the Brazilian shield and the contact of the Pantanal
277 floodplain with adjacent ecoregions. As an example, all species typical of Amazonian tropical
278 forests are assigned to BE2. Species of BE3 are limited to the left bank of Paraguay River and
279 BE4 is found on the opposite bank, not reaching, however, the river channel, a putative
280 barrier dated from the Miocene or earlier [21].
281 The physical barriers imposed by the Paraguay and Paraná rivers, as well as
282 vicariance across ecoregion boundaries, were associated with speciation events in
283 Neotropical marsupial linages [16] and may have also influenced the ranges of snakes in the
284 region under study. As an example, snakes in the genus Xenodon are found in different
285 biotic elements, on opposite margins of the Paraguay River: X. pulcher + X. semicinctus (BE4)
23
286 and X. dorbigny + X. histricus (BE3), a distribution pattern that may have resulted from the
287 appearance of this putative river barrier.
288 Moreover, vicariant processes may have also resulted from ecogeographical
289 patterns, as all snakes typical of the Amazonian rainforests are restricted to BE2, at the
290 northernmost portion of the study region. The congeneric Oxyrhopus guibei and O.
291 melanogenys, for example, despite belonging to partially overlapped biotic elements (BE1
292 and BE2, respectively), are found allopatrically in the Paraguay basin. Oxyrhopus
293 melanogenys, like most components of BE2, is typical of forested Amazonian areas [59]
294 whereas Oxyrhopus guibei is found in open areas and forest borders in central and
295 southeastern Brazil, in the Cerrado and Atlantic Forest domains [60].
296 The fact that we found clusters of phylogenetically related species in our biotic
297 elements only when considering the composite phylogeny and the latest relationships (NTI)
298 highlight that the distribution patterns of the snake fauna of PRB were shaped by different
299 forces, acting at different times and sometimes overlapping with each other. For example,
300 the cluster of BE3 for NTI and not for NRI is in agreement with the hypothesis that the
301 Paraguay River acted as a barrier in early times and the resulting biodiversity was exposed to
302 other process that affected in a different way the more recent relationships. It is important
303 to point out here that BE3 was the only biotic element with neither boundaries nor overlaps
304 with the Pantanal floodplain, and the only one showing a clustered pattern at some level.
305 Further evidence that points to the importance of vicariant events in the origin and
306 distribution of snakes in the Paraguay River basin is the fact that the richest areas of all four
307 biotic elements are located in ancient regions, geologically more stable than the lowlands.
308 Core areas of BE1, BE2 and BE3 are located on plateaus of the Brazilian Shield, of
309 Precambrian, Paleozoic and Mesozoic origins, respectively [20]. These plateaus were
310 probably isolated during Pleistocene climatic changes [61] and consequently have higher
311 levels of diversity and endemism than lower, geologically younger floodplain areas. The core
24
312 of BE4 is situated on the northwest of the Chaco plain, which although relatively lower than
313 plateaus of the Brazilian shield (core areas of BE1, BE2 and BE3), is geologically more ancient
314 and stable than the Pantanal floodplain, showing also high endemism and richness levels
315 [62, 63]. Such results are similar to those found in other Neotropical areas, namely the
316 Brazilian Cerrado and Caatinga [12, 15]. In these two ecoregions, the isolation of ancestral
317 faunas in elevated plateaus (“chapadas” in the Cerrado, “brejos” in the Caatinga) seemingly
318 played an important role in shaping local biotas, with resulting regionalization patterns
319 strongly linked to geomorphological features [4, 12, 15].
320 In our results, the Pantanal floodplain was associated with a single, wide-ranging
321 biotic element (BE1) formed by species found both in uplands and depressions, and
322 occurring almost throughout the Paraguay River basin. From a historical perspective, a
323 widely distributed biotic element can be the result of the lack of response to vicariant events
324 or the result of post-vicariant dispersal [17, 19]. In fact, there is general agreement that
325 some portions of rivers belonging to the Plata basin act as biogeographical corridors for
326 tropical biotas, including snakes [11, 64, 65]. Riparian humid forests and wetlands associated
327 to the Paraguay-Paraná river system promoted suitable microclimates and facilitated the the
328 dispersion and survival of tropical snakes species in temperate latitudes of South America,
329 mainly those exhibiting forested, semi-aquatic and aquatic habits [11]. Most aquatic species
330 of PRB belong to BE1 (e.g. Hydropsini and Hydrodynastini tribes), and these may have
331 dispersed using the river channel and associated riparian and floodplain areas. It also agrees
332 with patterns found at the Cerrado region where faunal interchange is more intense along
333 peripheral depressions and lowlands, while endemism is concentrated in upland plateaus
334 [12].
335 The current area of the Pantanal floodplain belonged to different ancient basins [20,
336 23], and the plateaus that currently act as its boundaries extended beyond their current
337 locations in the past [23, 66]. With the formation of the floodplain, some species that were
25
338 related to ancient uplands may have become extinct, had their distributions reduced, or
339 generated new sister species, as has been found for fishes [32] and rodents [14]. For the
340 latter, there is evidence of vicariant events and subsequent dispersion dated between 3 and
341 1.5 mya, splitting ancestral populations distributed throughout the current extension of the
342 Brazilian Cerrado and the southern portions of the Brazilian Caatinga. This event generated
343 several allopatric species- one of them nowadays occurring in the Pantanal and restricted to
344 the Paraguay River basin [14].
345 Our results show that the Pantanal snake fauna is poorer than surrounding faunas
346 and includes no exclusive snake species, as already recorded for other taxa [31]. The
347 Pantanal snake fauna consists almost entirely of species widely distributed in different
348 ecoregions within the Paraguay River basin, occupying both plateaus and depressions. No
349 biotic element in the basin is fully congruent with the Pantanal floodplain boundaries,
350 indicating that this is not a natural biogeographical unit for snakes. The spatial configuration
351 of three biotic elements (BE2, BE3 and BE4), adjacent to the Pantanal but beyond its limits,
352 suggests that the floodplain acts as a barrier to dispersal for at least some snake species, a
353 pattern also found in birds [10, 33].
354 Overall, our study indicates a complex and dual role of the Pantanal floodplain in
355 shaping regional snake distributions. The Pantanal floodplain and depressions may have split
356 ancestral upland ranges, while providing a dispersal corridor for taxa that occurred in both
357 upland and lowland riparian areas. Another plausible explanation is that the Pantanal acted
358 as an environmental filter, selecting parts of the ancestral biotas and separating ranges of
359 non-adapted taxa. These two major roles of the Pantanal floodplain can be further
360 elucidated by phylogenetic community assembly studies (e.g. [67), coupled with analyses of
361 speciation timing and extinction rates in this area. It is already recognized that flood pulses
362 drive important seasonal ecosystem changes, resulting in recurrent changes in the spatial
363 and temporal distributions of organisms, and affecting their life history strategies [31, 68].
26
364 These pulses are therefore considered a key process affecting both the functional and
365 taxonomic attributes of species assemblages in floodplains [30, 69, 70]. Herein we provide
366 the first study that considered a floodplain in a biogeographical perspective, as a potential
367 promoter of biotic diversification. If confirmed by further studies, our hypotheses may be
368 extended to other seasonally flooded regions and contribute to a better understanding of
369 the role of wetlands and major topographical units in shaping biogeographical patterns.
370 Of major relevance in detailing biogeographical patterns is their value as indicators
371 of biological singularity and of localized evolutionary processes [71-73]. These are crucial
372 concepts for understanding processes in the origin and maintenance of biotas, also
373 providing key spatial information for biodiversity conservation strategies [74, 75]. The
374 validity of biotic elements from the Paraguay River basin as not merely geographical but as
375 historical units should be tested in future studies. Our results indicate a strong
376 regionalization pattern of snake assemblages, in agreement with phylogenetic patterns and
377 the historical and ecogeographical structure of the basin. Regardless od their histoy, the
378 distribution patterns recovered herein should provide important testing grounds for
379 hypotheses based on distributional, phylogeographical or palaeoecological data that will
380 increase our knowledge of the origins and character of the rich and complex South American
381 biotas.
382 ACKNOWLEDGEMENTS
383 We thank the following curators of Brazilian museums for giving us access to the collections 384 under their care: Francisco Luís Franco (Coleção Herpetológica "Alphonse Richard Hoge", 385 Instituto Butantan); Guarino R. Colli (Coleção Herpetológica da Universidade de Brasília); 386 Gláucia Maria Funk Pontes (Museu de Ciência e Tecnologia da Pontifícia Universidade 387 Católica do Rio Grande do Sul); Paulo Roberto Manzani (Museu de Zoologia da Universidade 388 Estadual de Campinas "Adão José Cardoso"); Felipe Franco Curcio (Coleção Zoológica da 389 Universidade Federal de Mato Grosso); Gustavo Graciolli (Coleção Zoológica de Referência 390 da Universidade Federal de Mato Grosso do Sul); Hussam El Dine Zaher (Museu de Zoologia
27
391 da Universidade de São Paulo); Julio Cesar de Moura Leite (Museu de História Natural Capão 392 da Imbuia); Ronaldo Fernandes (Museu Nacional); Ana Lúcia da Costa Prudente (Museu 393 Paraense Emílio Goeldi). We are most grateful to Ricardo J. Sawaya, Malte Ebach, Daniel 394 Fernandes da Silva and two other anonymous reviewers for useful comments and insights, 395 and Jeffrey Himmelstein, Paula K. Rylands and Anthony Rylands for the English revision of 396 earlier drafts of the manuscript.
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521 44. Jadin RC, Burbrink FT, Rivas GA, Vitt LJ, Barrio-Amorós CL, Guralnick RP. Finding 522 arboreal snakes in an evolutionary tree: phylogenetic placement and systematic 523 revision of the Neotropical birdsnakes. J Zool Syst Evol Res. 2014; 52:257-264. 524 45. Hedges SB, Marion AB, Lipp KM, Marin J, Vidal N. A taxonomic framework for 525 typhlopid snakes from the Caribbean and other regions (Reptilia, Squamata). Caribb 526 Herpetol. 2014; 49:1-61. 527 46. Ronquist F, Sanmartín I. Phylogenetic Methods in Biogeography. Ann Rev Ecol Evol 528 Syst. 2011; 42:441-464. 529 47. Hennig C, Hausdorf B. A robust distance coefficient between distribution areas 530 incorporating geographic distances. Syst Biol. 2006; 55:170-175. 531 48. R Core Team R: a language and environment for statistical computing. Vienna: R 532 Foundation for Statistical Computing; 2014. Available: http://www.R-project.org/ 533 49. Fraley C, Raftery AEJ, Sloughter M Gneiting T. ensembleBMA: probabilistic 534 forecasting using ensembles and Bayesian Model Averaging. R package version 535 5.0.6. Seattle: University of Washington; 2014. Available: http://CRAN.R- 536 project.org/package=ensembleBMA/ 537 50. Fraley C, Raftery AE. How many clusters? Which clustering method? Answers via 538 model based cluster analysis. Comput J. 1998; 41:578-588. 539 51. Tonini JFR, Beard KH, Ferreira RB, Jetz W, Pyron RA. Fully-sampled phylogenies of 540 squamates reveal evolutionary patterns in threat status. Biol Conserv. 2016. 541 Available: http://dx.doi.org/10.1016/j.biocon.2016.03.039 542 52. Webb CO. Exploring the phylogenetic structure of ecological communities: an 543 example for rain forest trees. Am Nat. 2000; 156:145-55. 544 53. Kembel SW, Cowan PD, Helmus MR, Cornwell WK, Morlon H, Ackerly DD, et al. 545 Picante: R tools for integrating phylogenies and ecology. Bioinformatics. 2010; 546 26:1463-1464. 547 54. Maddison WP, Maddison DR. Mesquite: a modular system for evolutionary analysis. 548 Version 3.10. 2016. Available: http://mesquiteproject.org 549 55. Royer DL, Kooyman RM, Little SA, Wilf P. Ecology of leaf teeth: a multi-site analysis 550 from an Australian subtropical rainforest. Am J Bot. 2009; 96:738-750. 551 56. McGuire LP, Ratcliffe JM. Light enough to travel: migratory bats have smaller brains, 552 but not larger hippocampi, than sedentary species. Biol Letters. 2010; 7:233-236.
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586 70. Gerish M, Agostinelli V, Henle K, Dziock F. More species, but all do the same: 587 contrasting effects of flood disturbance on ground beetle functional and species 588 diversity. Oikos. 2012; 121:508-515. 589 71. Orme CDL, Davies RG, Burgess M, Eigenbrod F, Pickup N, Olson VA, et al. Global 590 hotspots of species richness are not congruent with endemism or threat. Nature. 591 2005; 436:1016-1019. 592 72. Wiens JJ. The niche, biogeography and species interactions. Philos Trans R Soc Lond 593 B Biol Sci. 2011; 366:2336-2350. 594 73. Aagesen L, Szumik C, Goloboff P. Consensus in the search for areas of endemism. J 595 Biogeogr. 2013; 40:2011-2016. 596 74. Whittaker RJ, Araújo MB, Jepson P, Ladle RJ, Watson JEM, Willis KJ. Conservation 597 biogeography: assessment and prospect. Divers Distrib. 2005; 11:3-23. 598 75. Richardson DM, Whittaker RJ. Conservation biogeography: foundations, concepts 599 and challenges. Divers Distrib. 2010; 16:313-320.
600 SUPPORTING INFORMATION
601 S1 Appendix. Study area and list of the zoological collections consulted. Maps showing the 602 limits of the Paraguay River basin and the ecoregions that it encompasses, along with the 603 Paraguay River channel, Pantanal wetland area, and the locality records for snakes in this 604 region, with list of the zoological collections consulted. 605 S2 Appendix. Phylogenies of the snakes of the Paraguay River basin. 606 S1 Table. Snake records dataset. Species presence-absence table in the 1 x 1 degree grid 607 cells covering the study area. The first two lines indicate coordinates of the centroid of each 608 cell. Cells marked with an asterisk are those in the Pantanal floodplain area.
34
609 SUPPORTING INFORMATION - The role of the Pantanal floodplain in the biogeographical 610 patterns of snakes in the Paraguay River basin, central South America
611 S1 Appendix. Study area and list of the zoological collections consulted.
612 Figure 1. Maps showing the limits of Paraguay River basin and the ecoregions that it 613 encompasses (right), and the Paraguay River channel, Pantanal wetland area, and locality 614 records of snakes in this region (left). Ecoregions sensu Olson DM, Dinerstein E, 615 Wikramanayake ED, Burgess ND, Powell GVN, Underwood EC, et al. Terrestrial ecoregions of 616 the world: a new map of life on Earth. Bioscience. 2001; 51: 933–938.
617 List of the zoological collections consulted 618 The Brazilian zoological collections consulted for locality records of snakes were: 619 • Coleção Herpetológica da Universidade de Brasília (CHUNB; Brasília) 620 • Coleção Zoológica da Universidade Federal de Mato Grosso (UFMT-R; Cuiabá) 621 • Coleção Zoológica de Referência da Universidade Federal de Mato Grosso do Sul 622 (ZUFMS; Campo Grande) 623 • Instituto Butantan (IBSP-Herpeto; São Paulo) 624 • Laboratório de Herpetologia da Universidade Federal de Mato Grosso – campus 625 Cuiabá (LH; Cuiabá)
35
626 • Museu de Ciência e Tecnologia da Pontifícia Universidade Católica do Rio Grande do 627 Sul (MCP-Répteis; Porto Alegre) 628 • Museu de História Natural Capão da Imbuia (MHNCI-Herpeto; Curitiba) 629 • Museu de Zoologia da Universidade de São Paulo (MZUSP; São Paulo) 630 • Museu de Zoologia da Universidade Estadual de Campinas (ZUEC-REP; Campinas) 631 • Museu Nacional (MNRJ; Rio de Janeiro), and Museu Paraense Emílio Goeldi (MPEG- 632 HOP; Belém) 633 • Seção de Herpetologia da Coleção Zoológica de Referência da Universidade Federal 634 de Mato Grosso do Sul – campus Corumbá (CEUCH; Corumbá)
36
635 S2 Appendix. Phylogenies of the snakes of the Paraguay River basin. 636 Pruned phylogeny 637 We used the phylogenetic relationships among species of Squamata recently 638 published by Tonini et al. 2016 [1]. We pruned the tree to include only the taxa that 639 registered to the Paraguay River Basin and retained the information on branch lengths.
640 641 Pruned phylogeny with 109 snake species from the Paraguay River Basin. Symbols at the tips 642 represent Biotic Elements, according to labels in the bottom right corner.
37
643 Composite phylogeny 644 The composite phylogeny used herein is based on previous studies from different 645 authors and include all species registered to the Paraguay River Basin. Tonini et al. 2016 [1] 646 and Pyron et al. 2013 [2] were used for the relative placement of snake families, subfamilies 647 and tribes. Phylogenetic placement of snake species of Paraguay River basin that were not 648 included in the available phylogenetic hypotheses were inferred according to the 649 relationships of sister species or included as polytomies in nodes containing their closely 650 related species. The phylogenies of Kluge 1991 [3], Rivera et al. 2011 [4], and Reynolds et al. 651 2014 [5] were used for assessing relationships within Boidae. Within Viperidae, the 652 phylogenies of Fenwick et al. 2009 [6] and Carrasco et al. 2012 [7] were used to determine 653 the overall relationships among Bothrops species, whereas Machado et al. 2014 [8] was 654 used for the relationships within Bothrops neuwiedii group. The relationships of species of 655 Elapidae were determined using the phylogeny of Silva and Sites 2001 [9], with the position 656 of Micrurus annellatus determined according to Slowinsky 1995 [10]. The relationships 657 within Colubridae follow Klaczko et al. 2014 [11]. The overall relationships within Dipsadidae 658 follow Grazziotin et al. 2012 [12], with the relationships within Pseudoboini following H. 659 Zaher (USP, São Paulo; personal communication).
38
660 661 Composite phylogeny of the 161 snake species from the Paraguay River Basin. Symbols at 662 the tips represent Biotic Elements, according to labels in the upper left corner
39
663 REFERENCES 664 1. Tonini JFR, Beard KH, Ferreira RB, Jetz W, Pyron A. Fully-sampled phylogenies of 665 squamates reveal evolutionary patterns in threat status. Biol Conserv. 2016; Available: 666 http://dx.doi.org/10.1016/j.biocon.2016.03.039 667 2. Pyron RA, Burbrink FT, Wiens JJ. A phylogeny and revised classification of Squamata, 668 including 4161 species of lizards and snakes. BMC Evol Biol. 2013; 13:93. 669 3. Kluge AG. Boinae snake phylogeny and research cycles. Misc publ - Mus Zool, Univ 670 Mich. 1991; 178:1-58. 671 4. Rivera PC, Di Cola V, Martínez JJ, Gardenal CN, Chiaraviglio M. Species delimitation 672 in the continental forms of the genus Epicrates (Serpentes, Boidae) integrating phylogenetics 673 and environmental niche models. PLoS ONE. 2011; 6:e22199. 674 5. Reynolds RG, Niemiller ML, Revell LJ. Toward a Tree-of-Life for the boas and 675 pythons: multilocus species-level phylogeny with unprecedented taxon sampling. Mol 676 Phylogenet Evol. 2014; 37:01-213. 677 6. Fenwick AM, Gutberlet RL, Evans JA, Parkinson CL. Morphological and molecular 678 evidence for phylogeny and classification of South American pitvipers, genera Bothrops, 679 Bothriopsis, and Bothrocophias (Serpentes: Viperidae). Zool J Linn Soc. 2009; 156:617-640. 680 7. Carrasco PA, Mattoni CI, Leynaud GC, Scrocchi GJ. Morphology, phylogeny and 681 taxonomy of South American bothropoid pitvipers (Serpentes, Viperidae). Zool Scripta. 682 2012; 41:109-124. 683 8. Machado T, Silva VX, Silva MJ. Phylogenetic relationships within Bothrops neuwiedii 684 group (Serpentes, Squamata): geographically highly-structured lineages, evidence of 685 introgressive hybridization and Neogene/Quaternary diversification. Mol Phylogenet Evol. 686 2014; 71:1-14. 687 9. Silva NJ, Sites JW. Phylogeny of South America triad coral snakes (Elapidae: 688 Micrurus) based on molecular characters. Herpetologica. 2001; 57:1-22. 689 10. Slowinski JB. A phylogenetic analysis of the New World coral snakes (Elapidae: 690 Leptomicrurus, Micruroides, and Micrurus) based on allozyme and morphological characters. 691 J Herpetol. 1995; 29:325-338. 692 11. Klaczko J, Montingelli GG, Zaher H. A combined morphological and molecular 693 phylogeny of the genus Chironius Fitzinger, 1826 (Serpentes: Colubridae). Zool J Linn Soc. 694 2014; 171:656-667.
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695 Grazziotin FG, Zaher H, Murphy RW, Scrocchi G, Benavides MA, Zhang YP et al. Molecular 696 phylogeny of the New World Dipsadidae (Serpentes: Colubroidea): a reappraisal. Cladistics. 697 2012; 28: 437-459
41
698 S1 Table. Snake records dataset.
699 Table S1. Species presence-absence table in the 1 x 1 degree grid cells covering the study
700 area. The first two lines indicate coordinates of the centroid of each cell. Cells marked with
701 an asterisk are those in the Pantanal floodplain area.
1 2 3 4 5 6 7 8 9 10 11 12 Latitude -27,5 -27,5 -26,5 -26,5 -26,5 -26,5 -26,5 -25,5 -25,5 -25,5 -25,5 -24,5 Longitude -58,5 -57,5 -59,5 -58,5 -57,5 -56,5 -55,5 -59,5 -58,5 -57,5 -56,5 -60,5 Anilius scytale 0 0 0 0 0 0 0 0 0 0 0 0 Apostolepis aff. nigroterminata1 0 0 0 0 0 0 0 0 0 0 0 0 Apostolepis aff. nigroterminata2 0 0 0 0 0 0 0 0 0 0 0 0 Apostolepis aff. nigroterminata3 0 0 0 0 0 0 0 0 0 0 0 0 Apostolepis ambiniger 0 0 0 0 0 0 0 0 0 1 0 0 Apostolepis assimilis 0 0 0 0 0 0 0 0 0 0 0 0 Apostolepis christineae 0 0 0 0 0 0 0 0 0 0 0 0 Apostolepis dimidiata 0 0 0 0 0 0 0 0 0 1 0 0 Apostolepis intermedia 0 0 0 0 0 0 0 0 0 0 0 0 Apostolepis sp1 0 0 0 0 0 0 0 0 0 0 0 0 Apostolepis vittata 0 0 0 0 0 0 0 0 0 0 0 0 Atractus albuquerquei 0 0 0 0 0 0 0 0 0 0 0 0 Atractus paraguayensis 0 0 0 1 0 0 0 0 0 0 0 0 Atractus reticulatus 0 0 0 0 0 0 1 0 0 1 0 0 Atractus thalesdelemai 0 0 0 0 0 0 1 0 0 0 0 0 Boa constrictor 0 0 0 0 0 0 0 0 0 1 0 0 Boiruna maculata 0 1 0 1 0 0 0 1 1 1 0 0 Bothrops alternatus 0 1 0 1 1 1 0 0 1 1 1 0 Bothrops diporus 0 0 0 0 0 1 1 1 1 1 1 1 Bothrops jararaca 0 0 0 0 0 0 0 0 0 0 0 0 Bothrops jararacussu 0 0 0 0 0 0 0 0 0 0 1 0 Bothrops mattogrossensis 0 0 0 1 0 1 0 0 0 0 1 0 Bothrops moojeni 0 0 0 0 0 0 0 0 0 0 1 0 Bothrops pauloensis 0 0 0 0 0 1 1 0 0 0 0 0 Chironius bicarinatus 0 0 0 0 0 0 0 0 0 0 0 0 Chironius exoletus 0 0 0 0 0 0 0 0 0 0 0 0 Chironius flavolineatus 0 0 0 0 0 0 0 0 0 0 0 0 Chironius fuscus 0 0 0 0 0 0 0 0 0 0 0 0 Chironius laurenti 0 0 0 0 0 0 0 0 0 0 0 0 Chironius quadricarinatus 0 0 0 1 0 0 0 0 0 1 0 0 Chironius scurrulus 0 0 0 0 0 0 0 0 0 0 0 0
42
13 14 15 16 17 18 19 20 21 22 23 24 Latitude -24,5 -24,5 -24,5 -24,5 -24,5 -23,5 -23,5 -23,5 -23,5 -23,5 -23,5 -23,5 Longitude -59,5 -58,5 -57,5 -56,5 -55,5 -63,5 -61,5 -60,5 -59,5 -58,5 -57,5 -56,5 Anilius scytale 0 0 0 0 0 0 0 0 0 0 0 0 Apostolepis aff. nigroterminata1 0 0 0 0 0 0 0 0 0 0 0 0 Apostolepis aff. nigroterminata2 0 0 0 0 0 0 0 0 0 0 0 0 Apostolepis aff. nigroterminata3 0 0 0 0 0 0 0 0 0 0 0 0 Apostolepis ambiniger 0 0 0 0 0 0 0 0 0 0 0 0 Apostolepis assimilis 0 0 0 0 0 0 0 0 0 0 0 0 Apostolepis christineae 0 0 0 0 0 0 0 0 0 0 0 0 Apostolepis dimidiata 0 0 0 1 0 0 0 0 0 0 0 0 Apostolepis intermedia 0 0 0 0 0 0 0 0 0 0 0 0 Apostolepis sp1 0 0 0 0 0 0 0 0 0 0 0 0 Apostolepis vittata 0 0 0 0 0 0 0 0 0 0 0 0 Atractus albuquerquei 0 0 0 0 0 0 0 0 0 0 0 0 Atractus paraguayensis 0 0 0 1 0 0 0 0 0 0 0 0 Atractus reticulatus 0 0 0 0 0 0 0 0 0 0 0 0 Atractus thalesdelemai 0 0 0 0 0 0 0 0 0 0 0 0 Boa constrictor 0 1 0 1 1 0 0 0 0 1 0 0 Boiruna maculata 0 1 1 0 0 1 1 0 0 0 0 0 Bothrops alternatus 0 0 1 1 0 0 0 0 0 0 1 0 Bothrops diporus 1 1 0 0 0 0 0 1 0 0 0 0 Bothrops jararaca 0 0 0 0 1 0 0 0 0 0 0 0 Bothrops jararacussu 0 0 0 0 0 0 0 0 0 0 0 0 Bothrops mattogrossensis 0 1 0 0 0 0 0 1 1 1 1 0 Bothrops moojeni 0 0 0 0 1 0 0 0 0 0 0 0 Bothrops pauloensis 0 0 0 0 0 0 0 0 0 0 0 0 Chironius bicarinatus 0 0 0 0 0 0 0 0 0 0 0 0 Chironius exoletus 0 0 0 0 0 0 0 0 0 0 0 0 Chironius flavolineatus 0 0 0 0 0 0 0 0 0 0 0 0 Chironius fuscus 0 0 0 0 0 0 0 0 0 0 0 0 Chironius laurenti 0 0 0 0 0 0 0 0 0 0 0 0 Chironius quadricarinatus 0 0 0 1 0 1 0 1 1 1 1 0 Chironius scurrulus 0 0 0 0 0 0 0 0 0 0 0 0
43
25 26 27 28 29 30 31 32* 33 34 35 36 Latitude -23,5 -22,5 -22,5 -22,5 -22,5 -22,5 -22,5 -22,5 -22,5 -22,5 -21,5 -21,5 Longitude -55,5 -63,5 -62,5 -61,5 -60,5 -59,5 -58,5 -57,5 -56,5 -55,5 -65,5 -63,5 Anilius scytale 0 0 0 0 0 0 0 0 0 0 0 0 Apostolepis aff. nigroterminata1 0 0 0 0 0 0 0 0 0 0 0 0 Apostolepis aff. nigroterminata2 0 0 0 0 0 0 0 0 0 0 0 0 Apostolepis aff. nigroterminata3 0 0 0 0 0 0 0 0 0 0 0 0 Apostolepis ambiniger 0 0 0 0 0 0 0 0 1 0 0 0 Apostolepis assimilis 0 0 0 0 0 0 0 0 0 0 0 0 Apostolepis christineae 0 0 0 0 0 0 0 0 0 0 0 0 Apostolepis dimidiata 0 0 0 0 0 0 0 0 1 0 0 0 Apostolepis intermedia 0 0 0 0 0 0 0 0 0 0 0 0 Apostolepis sp1 0 0 0 0 0 0 0 0 0 0 0 0 Apostolepis vittata 0 0 0 0 0 0 0 0 0 0 0 0 Atractus albuquerquei 0 0 0 0 0 0 0 0 0 0 0 0 Atractus paraguayensis 0 0 0 0 0 0 0 0 0 0 0 0 Atractus reticulatus 0 0 0 0 0 0 0 0 0 0 0 0 Atractus thalesdelemai 0 0 0 0 0 0 0 0 0 0 0 0 Boa constrictor 0 0 0 0 1 1 0 1 1 0 1 0 Boiruna maculata 0 1 0 0 1 0 0 0 0 0 0 0 Bothrops alternatus 0 0 0 0 1 0 0 0 0 0 0 0 Bothrops diporus 0 0 0 0 1 0 0 0 0 0 0 0 Bothrops jararaca 0 0 0 0 0 0 0 0 0 0 0 0 Bothrops jararacussu 0 0 0 0 0 0 0 0 0 0 0 0 Bothrops mattogrossensis 0 0 0 0 1 1 0 1 1 1 0 0 Bothrops moojeni 1 0 0 0 0 0 0 0 1 0 0 0 Bothrops pauloensis 1 0 0 0 0 0 1 1 1 0 0 0 Chironius bicarinatus 0 0 0 0 0 0 0 0 0 0 0 0 Chironius exoletus 0 0 0 0 0 0 0 0 0 0 0 0 Chironius flavolineatus 0 0 0 0 0 0 0 0 1 0 0 0 Chironius fuscus 0 0 0 0 0 0 0 0 0 0 0 0 Chironius laurenti 0 0 0 0 0 0 0 0 0 0 0 0 Chironius quadricarinatus 0 0 0 0 1 1 0 1 1 1 0 0 Chironius scurrulus 0 0 0 0 0 0 0 0 0 0 0 0
44
37 38 39 40 41 42* 43 44 45 46 47 48 Latitude -21,5 -21,5 -21,5 -21,5 -21,5 -21,5 -21,5 -21,5 -20,5 -20,5 -20,5 -20,5 Longitude -62,5 -61,5 -60,5 -59,5 -58,5 -57,5 -56,5 -55,5 -65,5 -64,5 -63,5 -62,5 Anilius scytale 0 0 0 0 0 0 0 0 0 0 0 0 Apostolepis aff. nigroterminata1 0 0 0 0 0 0 0 0 0 0 0 0 Apostolepis aff. nigroterminata2 0 0 0 0 0 0 0 0 0 0 0 0 Apostolepis aff. nigroterminata3 0 0 0 0 0 0 0 0 0 0 0 0 Apostolepis ambiniger 0 0 0 0 0 0 0 0 0 0 0 1 Apostolepis assimilis 0 0 0 0 0 0 0 0 0 0 0 0 Apostolepis christineae 0 0 0 0 0 0 0 0 0 0 0 0 Apostolepis dimidiata 0 0 0 0 0 0 1 0 0 0 0 0 Apostolepis intermedia 0 0 0 0 0 0 0 0 0 0 0 0 Apostolepis sp1 0 0 0 0 0 0 0 0 0 0 0 0 Apostolepis vittata 0 0 0 0 0 0 0 0 0 0 0 0 Atractus albuquerquei 0 0 0 0 0 0 0 0 0 0 0 0 Atractus paraguayensis 0 0 0 0 0 0 0 0 0 0 0 0 Atractus reticulatus 0 0 0 0 0 0 0 0 0 0 0 0 Atractus thalesdelemai 0 0 0 0 0 0 0 0 0 0 0 0 Boa constrictor 0 1 1 0 0 1 1 0 0 0 0 0 Boiruna maculata 0 1 1 0 0 1 0 0 0 0 0 1 Bothrops alternatus 0 0 0 0 0 0 1 0 0 0 0 0 Bothrops diporus 0 0 0 0 0 0 0 0 0 0 0 0 Bothrops jararaca 0 0 0 0 0 0 0 0 0 0 0 0 Bothrops jararacussu 0 0 0 0 0 0 0 0 0 0 0 0 Bothrops mattogrossensis 0 0 0 0 1 1 1 0 0 0 0 0 Bothrops moojeni 0 0 0 0 0 1 0 1 0 0 0 0 Bothrops pauloensis 0 0 0 0 0 0 1 1 0 0 0 0 Chironius bicarinatus 0 0 0 0 0 0 1 0 0 0 0 0 Chironius exoletus 0 0 0 0 0 0 0 0 0 0 0 0 Chironius flavolineatus 0 0 0 0 0 0 1 1 0 0 0 0 Chironius fuscus 0 0 0 0 0 0 0 0 0 0 0 0 Chironius laurenti 0 0 0 0 0 0 0 0 0 0 0 0 Chironius quadricarinatus 0 0 0 0 0 0 1 0 0 0 0 0 Chironius scurrulus 0 0 0 0 0 0 0 0 0 0 0 0
45
49 50 51 52* 53 54 55 56 57 58 59 60* Latitude -20,5 -20,5 -20,5 -20,5 -20,5 -20,5 -20,5 -20,5 -19,5 -19,5 -19,5 -19,5 Longitude -61,5 -60,5 -59,5 -58,5 -57,5 -56,5 -55,5 -54,5 -65,5 -60,5 -59,5 -58,5 Anilius scytale 0 0 0 0 0 0 0 0 0 0 0 0 Apostolepis aff. nigroterminata1 0 0 0 0 0 0 0 0 0 0 0 0 Apostolepis aff. nigroterminata2 0 0 0 0 0 0 0 0 0 0 0 0 Apostolepis aff. nigroterminata3 0 0 0 0 0 0 0 0 0 0 0 0 Apostolepis ambiniger 0 0 0 0 0 1 0 0 1 0 0 0 Apostolepis assimilis 0 0 0 0 0 0 0 0 0 0 0 0 Apostolepis christineae 0 0 0 0 0 0 0 0 0 0 0 0 Apostolepis dimidiata 0 0 0 0 0 1 1 0 0 0 0 0 Apostolepis intermedia 0 0 0 0 0 1 1 0 0 0 0 0 Apostolepis sp1 0 0 0 0 0 0 0 0 0 0 0 0 Apostolepis vittata 0 0 0 0 0 0 0 0 0 0 0 0 Atractus albuquerquei 0 0 0 0 0 0 0 0 0 0 0 0 Atractus paraguayensis 0 0 0 0 0 0 0 0 0 0 0 0 Atractus reticulatus 0 0 0 0 0 0 0 0 0 0 0 0 Atractus thalesdelemai 0 0 0 0 0 0 0 0 0 0 0 0 Boa constrictor 0 1 0 0 1 1 1 1 0 0 0 0 Boiruna maculata 0 1 1 0 0 1 0 0 0 1 1 0 Bothrops alternatus 0 0 0 0 0 1 1 1 0 0 0 0 Bothrops diporus 0 1 0 0 0 0 0 0 0 0 0 0 Bothrops jararaca 0 0 0 0 0 0 0 0 0 0 0 0 Bothrops jararacussu 0 0 0 0 0 0 0 0 0 0 0 0 Bothrops mattogrossensis 0 1 1 1 1 1 1 0 0 0 0 1 Bothrops moojeni 0 0 0 1 0 1 1 1 0 0 0 0 Bothrops pauloensis 0 0 0 0 0 1 1 1 0 0 0 0 Chironius bicarinatus 0 0 0 0 0 0 1 0 0 0 0 0 Chironius exoletus 0 0 0 0 0 0 1 0 0 0 0 0 Chironius flavolineatus 0 0 0 0 0 1 1 0 0 0 0 0 Chironius fuscus 0 0 0 0 0 0 0 0 0 0 0 0 Chironius laurenti 0 0 0 0 0 0 0 0 0 0 0 0 Chironius quadricarinatus 0 1 0 0 0 1 1 0 0 0 0 0 Chironius scurrulus 0 0 0 0 0 0 0 0 0 0 0 0
46
61* 62* 63* 64 65 66 67 68* 69* 70* 71 72 Latitude -19,5 -19,5 -19,5 -19,5 -19,5 -18,5 -18,5 -18,5 -18,5 -18,5 -18,5 -18,5 Longitude -57,5 -56,5 -55,5 -54,5 -53,5 -59,5 -58,5 -57,5 -56,5 -55,5 -54,5 -53,5 Anilius scytale 0 0 0 0 0 0 0 0 0 0 0 0 Apostolepis aff. nigroterminata1 1 0 0 0 0 0 0 0 0 0 0 0 Apostolepis aff. nigroterminata2 0 0 0 0 0 0 0 0 0 0 0 0 Apostolepis aff. nigroterminata3 0 0 0 0 0 0 0 0 0 0 0 0 Apostolepis ambiniger 0 0 0 0 0 0 0 0 0 0 0 0 Apostolepis assimilis 0 0 0 0 0 0 0 0 0 0 0 0 Apostolepis christineae 0 0 0 0 0 0 0 0 0 0 0 0 Apostolepis dimidiata 0 0 1 0 0 0 0 0 0 0 0 0 Apostolepis intermedia 0 0 0 0 0 0 0 0 0 0 0 0 Apostolepis sp1 1 0 0 0 0 0 0 0 1 0 0 0 Apostolepis vittata 0 0 0 0 0 0 0 1 0 0 0 0 Atractus albuquerquei 0 0 0 0 0 0 0 0 0 0 0 0 Atractus paraguayensis 0 0 0 0 0 0 0 0 0 0 0 0 Atractus reticulatus 0 0 0 0 0 0 0 0 0 0 0 0 Atractus thalesdelemai 0 0 0 0 0 0 0 0 0 0 0 0 Boa constrictor 1 1 1 0 0 0 0 1 0 1 0 0 Boiruna maculata 1 1 0 0 0 0 0 1 1 0 0 0 Bothrops alternatus 0 0 1 0 0 0 0 0 0 0 0 0 Bothrops diporus 0 0 0 0 0 0 0 0 0 0 0 0 Bothrops jararaca 0 0 0 0 0 0 0 0 0 0 0 0 Bothrops jararacussu 0 0 0 0 0 0 0 0 0 0 0 0 Bothrops mattogrossensis 1 1 1 1 0 0 0 1 1 1 1 0 Bothrops moojeni 1 1 1 1 1 0 0 1 0 0 1 1 Bothrops pauloensis 1 0 1 0 0 0 0 0 0 0 1 0 Chironius bicarinatus 0 0 1 0 0 0 0 0 0 0 1 0 Chironius exoletus 1 0 0 0 0 0 0 0 0 0 0 1 Chironius flavolineatus 1 1 1 1 0 0 0 1 1 0 0 1 Chironius fuscus 1 0 0 0 0 0 0 0 0 0 0 0 Chironius laurenti 1 0 0 0 0 0 0 1 0 0 0 0 Chironius quadricarinatus 1 1 1 0 0 0 0 1 0 0 0 0 Chironius scurrulus 0 0 0 0 0 0 0 0 0 0 0 0
47
73* 74* 75 76 77 78* 79* 80* 81 82 83 84 Latitude -17,5 -17,5 -17,5 -17,5 -16,5 -16,5 -16,5 -16,5 -16,5 -15,5 -15,5 -15,5 Longitude -57,5 -56,5 -54,5 -53,5 -58,5 -57,5 -56,5 -55,5 -54,5 -58,5 -57,5 -56,5 Anilius scytale 0 0 0 0 0 0 0 0 0 1 1 1 Apostolepis aff. nigroterminata1 0 0 0 0 0 0 0 0 0 0 0 0 Apostolepis aff. nigroterminata2 1 0 0 0 0 0 0 0 0 0 0 0 Apostolepis aff. nigroterminata3 0 0 0 0 0 0 1 0 0 0 0 0 Apostolepis ambiniger 0 0 0 0 0 0 0 0 0 0 0 0 Apostolepis assimilis 0 0 1 0 0 0 1 0 0 0 0 1 Apostolepis christineae 0 0 0 0 0 1 0 0 0 0 1 0 Apostolepis dimidiata 0 0 0 0 0 0 0 0 0 0 0 0 Apostolepis intermedia 0 0 0 0 0 0 0 0 0 0 0 0 Apostolepis sp1 0 0 0 0 0 0 0 0 0 0 0 0 Apostolepis vittata 0 0 0 0 0 0 0 0 0 0 0 0 Atractus albuquerquei 0 0 1 0 0 0 0 0 0 0 1 0 Atractus paraguayensis 0 0 0 0 0 0 0 0 0 0 0 0 Atractus reticulatus 0 0 0 0 0 0 0 0 0 0 0 0 Atractus thalesdelemai 0 0 0 0 0 0 0 0 0 0 0 0 Boa constrictor 1 0 1 0 0 1 0 0 0 0 0 1 Boiruna maculata 0 0 0 0 0 0 1 0 0 0 0 0 Bothrops alternatus 0 0 0 0 0 0 0 0 0 0 0 0 Bothrops diporus 0 0 0 0 0 0 0 0 0 0 0 0 Bothrops jararaca 0 0 0 0 0 0 0 0 0 0 0 0 Bothrops jararacussu 0 0 0 0 0 0 0 0 0 0 0 0 Bothrops mattogrossensis 0 0 1 0 1 1 1 0 0 1 0 1 Bothrops moojeni 1 1 1 0 1 1 1 0 0 1 1 1 Bothrops pauloensis 0 0 1 0 0 0 0 0 0 0 0 1 Chironius bicarinatus 0 0 0 0 0 0 0 0 0 0 0 0 Chironius exoletus 1 0 0 0 0 1 0 0 0 0 1 0 Chironius flavolineatus 1 0 1 0 1 1 0 0 0 1 1 1 Chironius fuscus 0 0 0 0 0 0 0 0 0 1 0 0 Chironius laurenti 1 0 0 0 0 1 1 0 1 0 1 1 Chironius quadricarinatus 0 0 0 0 0 1 1 0 0 0 0 1 Chironius scurrulus 0 0 0 0 0 0 0 0 0 1 1 0
48
85 86 87 88 89 Latitude -15,5 -15,5 -14,5 -14,5 -14,5 Longitude -55,5 -54,5 -57,5 -56,5 -55,5 Anilius scytale 0 0 0 0 1 Apostolepis aff. nigroterminata1 0 0 0 0 0 Apostolepis aff. nigroterminata2 0 0 0 0 0 Apostolepis aff. nigroterminata3 0 0 0 0 0 Apostolepis ambiniger 0 0 0 0 0 Apostolepis assimilis 1 0 0 0 1 Apostolepis christineae 0 0 0 0 0 Apostolepis dimidiata 0 0 0 0 0 Apostolepis intermedia 0 0 0 0 0 Apostolepis sp1 0 0 0 0 0 Apostolepis vittata 1 0 0 0 0 Atractus albuquerquei 0 0 0 0 0 Atractus paraguayensis 0 0 0 0 0 Atractus reticulatus 0 0 0 0 0 Atractus thalesdelemai 0 0 0 0 0 Boa constrictor 1 0 1 0 1 Boiruna maculata 0 0 0 0 1 Bothrops alternatus 0 0 0 0 0 Bothrops diporus 0 0 0 0 0 Bothrops jararaca 0 0 0 0 0 Bothrops jararacussu 0 0 0 0 0 Bothrops mattogrossensis 0 0 0 0 1 Bothrops moojeni 1 0 1 1 1 Bothrops pauloensis 1 0 0 1 1 Chironius bicarinatus 0 0 0 0 0 Chironius exoletus 1 0 0 0 1 Chironius flavolineatus 1 0 0 0 1 Chironius fuscus 0 0 0 0 0 Chironius laurenti 0 0 0 0 0 Chironius quadricarinatus 1 0 0 0 1 Chironius scurrulus 0 0 0 0 0
49
1 2 3 4 5 6 7 8 9 10 11 12 Clelia clelia 0 1 1 1 0 0 0 0 0 1 1 0 Clelia langeri 0 0 0 0 0 0 0 0 0 0 0 0 Clelia plumbea 0 0 0 0 0 0 0 0 0 1 0 0 Corallus hortulanus 0 0 0 0 0 0 0 0 0 0 0 0 Crotalus durissus 1 0 1 1 0 1 0 1 1 1 0 0 Dipsas bucephala 0 0 0 0 0 0 0 0 0 0 1 0 Dipsas catesbyi 0 0 0 0 0 0 0 0 0 0 0 0 Dipsas indica 0 0 0 0 0 0 0 0 0 0 0 0 Drepanoides anomalus 0 0 0 0 0 0 0 0 0 0 0 0 Drymarchon corais 0 0 0 0 0 0 0 0 0 0 0 0 Drymoluber brazili 0 0 0 0 0 0 0 0 0 0 0 0 Epicrates alvarezi 0 0 0 0 0 0 0 0 0 0 0 0 Epicrates cenchria 0 0 0 0 0 0 0 0 0 0 0 0 Epicrates crassus 0 0 0 0 0 0 0 0 0 0 1 0 Epictia aff. tenella 0 0 0 0 0 0 0 0 0 0 0 0 Epictia albipuncta 0 0 0 0 0 0 0 0 0 1 0 0 Epictia munoai 0 0 0 0 0 0 0 0 0 1 0 0 Epictia vellardi 0 0 0 1 0 0 0 0 0 0 0 0 Erythrolamprus aesculapii 0 0 0 0 0 1 1 0 0 1 1 0 Erythrolamprus albertguentheri 0 0 0 0 0 0 0 0 0 0 0 0 Erythrolamprus almadensis 0 0 0 1 0 0 1 0 0 1 0 0 Erythrolamprus frenatus 0 0 0 0 0 0 0 0 0 0 0 0 Erythrolamprus jaegeri 0 0 0 1 0 0 0 0 0 1 1 0 Erythrolamprus maryellenae 0 0 0 0 0 0 0 0 0 0 0 0 Erythrolamprus miliaris 0 0 0 0 0 0 1 0 0 0 1 0 Erythrolamprus poecilogyrus 0 0 0 1 0 1 0 0 0 1 1 0 Erythrolamprus reginae 0 0 0 0 0 1 0 0 0 1 0 0 Erythrolamprus sagittifer 0 0 0 0 0 0 0 0 0 0 1 0 Erythrolamprus semiaureus 0 0 0 1 0 0 1 0 0 1 0 0 Erythrolamprus taeniogaster 0 0 0 0 0 0 0 0 0 0 0 0 Erythrolamprus typhlus 0 0 0 0 0 0 0 0 0 0 0 0 Eunectes murinus 0 0 0 0 0 0 0 0 0 0 0 0 Eunectes notaeus 1 0 0 1 0 0 0 0 0 1 0 0 Helicops angulatus 0 0 0 0 0 0 0 0 0 0 0 0 Helicops infrataeniatus 0 0 0 0 0 0 1 0 0 0 0 0 Helicops leopardinus 0 0 0 1 0 0 0 0 0 1 0 0 Helicops modestus 0 0 0 0 0 0 0 0 0 0 0 0 Helicops polylepis 0 0 0 0 0 0 0 0 0 0 0 0 Hydrodynastes bicinctus 0 0 0 0 0 0 0 0 0 0 0 0 Hydrodynastes gigas 0 0 0 1 1 0 1 0 0 1 0 0 Hydrops caesurus 0 0 0 0 0 0 0 0 0 0 0 0 Imantodes cenchoa 0 0 0 0 0 0 0 0 0 0 0 0 Leptodeira annulata 1 0 0 1 0 0 0 0 0 1 0 0 Leptophis ahaetulla 0 0 0 1 1 0 1 0 0 1 1 0 Liotyphlops beui 0 0 0 0 0 0 0 0 0 0 0 0
50
13 14 15 16 17 18 19 20 21 22 23 24 Clelia clelia 0 1 0 0 0 0 0 0 0 1 0 0 Clelia langeri 0 0 0 0 0 0 0 0 0 0 0 0 Clelia plumbea 0 0 0 0 0 0 0 0 0 0 0 0 Corallus hortulanus 0 0 0 0 0 0 0 0 0 0 0 0 Crotalus durissus 0 1 0 1 1 0 0 0 1 0 0 1 Dipsas bucephala 0 0 0 0 0 0 0 0 0 0 0 0 Dipsas catesbyi 0 0 0 0 0 0 0 0 0 0 0 0 Dipsas indica 0 0 0 0 0 0 0 0 0 0 0 0 Drepanoides anomalus 0 0 0 0 0 0 0 0 0 0 0 0 Drymarchon corais 0 1 0 1 0 0 0 1 1 1 0 0 Drymoluber brazili 0 0 0 0 1 0 0 0 0 0 0 0 Epicrates alvarezi 0 0 0 0 0 0 0 0 0 0 0 0 Epicrates cenchria 0 0 0 0 0 0 0 0 0 0 0 0 Epicrates crassus 0 0 0 1 1 0 0 0 0 0 0 0 Epictia aff. tenella 0 0 0 0 0 0 0 0 0 0 0 0 Epictia albipuncta 0 1 0 1 0 0 0 0 1 0 1 0 Epictia munoai 0 1 0 0 0 0 0 0 1 0 1 0 Epictia vellardi 0 0 0 0 0 0 0 0 0 0 0 0 Erythrolamprus aesculapii 0 0 0 1 1 0 0 0 0 0 1 1 Erythrolamprus albertguentheri 0 0 0 0 0 0 0 1 1 0 0 0 Erythrolamprus almadensis 0 1 0 1 0 0 1 0 0 0 0 1 Erythrolamprus frenatus 0 0 0 1 0 0 0 0 0 0 0 0 Erythrolamprus jaegeri 0 1 1 1 0 0 0 0 1 0 0 0 Erythrolamprus maryellenae 0 0 0 0 0 0 0 0 0 0 0 0 Erythrolamprus miliaris 0 0 0 0 0 0 0 0 0 0 0 0 Erythrolamprus poecilogyrus 1 1 1 1 1 0 1 1 1 1 1 1 Erythrolamprus reginae 0 1 1 1 1 0 0 0 0 0 0 0 Erythrolamprus sagittifer 0 0 0 0 0 1 0 0 1 1 0 0 Erythrolamprus semiaureus 0 0 0 1 0 0 0 0 1 0 0 0 Erythrolamprus taeniogaster 0 0 0 0 0 0 0 0 0 0 0 0 Erythrolamprus typhlus 0 0 0 0 0 0 0 0 0 0 0 1 Eunectes murinus 0 0 0 0 0 0 0 0 0 0 0 1 Eunectes notaeus 0 1 0 1 0 0 0 1 1 0 1 0 Helicops angulatus 0 0 0 0 0 0 0 0 0 0 0 0 Helicops infrataeniatus 0 0 0 0 0 0 0 0 0 0 0 0 Helicops leopardinus 0 1 1 1 0 0 0 0 1 0 1 0 Helicops modestus 0 0 0 0 0 0 0 0 0 0 0 0 Helicops polylepis 0 0 0 0 0 0 0 0 0 0 0 0 Hydrodynastes bicinctus 0 0 0 0 0 0 0 0 0 0 0 0 Hydrodynastes gigas 0 1 1 1 0 0 0 1 1 1 0 0 Hydrops caesurus 0 0 1 0 1 0 0 0 0 0 0 0 Imantodes cenchoa 0 0 0 0 0 0 0 0 0 0 0 0 Leptodeira annulata 0 0 0 0 0 0 1 1 1 0 1 0 Leptophis ahaetulla 0 1 0 1 1 0 0 0 1 1 0 0 Liotyphlops beui 0 0 0 0 0 0 0 0 0 0 0 0
51
25 26 27 28 29 30 31 32* 33 34 35 36 Clelia clelia 1 0 0 0 0 0 0 1 0 0 0 1 Clelia langeri 0 0 0 0 0 0 0 0 0 0 0 0 Clelia plumbea 0 0 0 0 0 0 0 0 0 0 0 0 Corallus hortulanus 0 0 0 0 0 0 0 0 0 0 0 0 Crotalus durissus 0 0 0 0 1 1 0 1 1 1 0 0 Dipsas bucephala 0 0 0 0 0 0 0 0 0 0 0 0 Dipsas catesbyi 0 0 0 0 0 0 0 0 0 0 0 0 Dipsas indica 0 0 0 0 0 0 0 0 0 0 0 0 Drepanoides anomalus 0 0 0 0 0 0 0 0 0 0 0 0 Drymarchon corais 0 0 0 0 0 0 0 1 1 0 0 0 Drymoluber brazili 0 0 0 0 0 0 0 0 0 0 0 0 Epicrates alvarezi 0 0 0 0 1 1 0 0 0 0 0 0 Epicrates cenchria 0 0 0 0 0 0 0 0 0 0 0 0 Epicrates crassus 0 0 0 0 0 0 0 0 0 0 0 0 Epictia aff. tenella 0 0 0 0 0 0 0 0 0 0 0 0 Epictia albipuncta 0 0 0 0 1 0 0 0 0 0 0 0 Epictia munoai 0 0 0 0 1 0 0 0 0 0 0 0 Epictia vellardi 0 0 0 0 0 0 0 0 0 0 0 0 Erythrolamprus aesculapii 0 0 0 0 0 0 0 0 1 0 0 0 Erythrolamprus albertguentheri 0 0 0 1 1 0 0 0 0 0 0 1 Erythrolamprus almadensis 0 0 0 0 0 0 0 0 1 0 0 0 Erythrolamprus frenatus 0 0 0 0 0 0 0 0 0 1 0 0 Erythrolamprus jaegeri 1 0 0 0 0 0 0 0 0 0 0 0 Erythrolamprus maryellenae 0 0 0 0 0 0 0 0 0 0 0 0 Erythrolamprus miliaris 0 0 0 0 0 0 0 0 0 0 0 0 Erythrolamprus poecilogyrus 0 0 1 1 1 1 1 0 1 1 1 1 Erythrolamprus reginae 0 0 0 0 0 0 0 0 0 0 0 0 Erythrolamprus sagittifer 0 0 1 0 1 1 0 0 0 0 1 1 Erythrolamprus semiaureus 0 0 0 0 0 0 0 0 0 0 0 0 Erythrolamprus taeniogaster 0 0 0 0 0 0 0 0 0 0 0 0 Erythrolamprus typhlus 0 0 0 0 0 0 0 0 0 0 0 1 Eunectes murinus 0 0 0 0 0 0 0 0 1 0 0 0 Eunectes notaeus 0 0 0 0 0 0 0 1 0 0 0 0 Helicops angulatus 0 0 0 0 0 0 0 0 0 0 0 0 Helicops infrataeniatus 0 0 0 0 0 0 0 0 1 0 0 0 Helicops leopardinus 0 0 0 0 0 0 0 0 1 0 0 0 Helicops modestus 0 0 0 0 0 0 0 0 0 0 0 0 Helicops polylepis 0 0 0 0 0 0 0 0 0 0 0 0 Hydrodynastes bicinctus 0 0 0 0 0 0 0 0 0 0 0 0 Hydrodynastes gigas 0 0 0 0 0 1 1 1 0 0 0 0 Hydrops caesurus 0 0 0 0 0 0 0 0 0 0 0 0 Imantodes cenchoa 0 1 0 0 0 0 0 1 0 0 0 0 Leptodeira annulata 0 0 1 1 1 1 0 1 0 0 1 0 Leptophis ahaetulla 0 0 0 0 0 0 0 0 1 1 1 0 Liotyphlops beui 0 0 0 0 0 0 0 0 1 0 0 0
52
37 38 39 40 41 42* 43 44 45 46 47 48 Clelia clelia 0 0 0 0 0 0 0 0 0 0 0 0 Clelia langeri 0 0 0 0 0 0 0 0 0 1 0 0 Clelia plumbea 0 0 0 0 0 0 0 0 0 0 0 0 Corallus hortulanus 0 0 0 0 0 0 0 0 0 0 0 0 Crotalus durissus 0 1 0 0 0 1 1 1 0 0 0 1 Dipsas bucephala 0 0 0 0 0 0 0 0 0 0 0 0 Dipsas catesbyi 0 0 0 0 0 0 0 0 0 0 0 0 Dipsas indica 0 0 0 0 0 0 0 0 0 0 0 0 Drepanoides anomalus 0 0 0 0 0 0 0 0 0 0 0 0 Drymarchon corais 0 1 0 1 1 1 1 0 0 0 0 0 Drymoluber brazili 0 0 0 0 0 0 0 0 0 0 0 0 Epicrates alvarezi 0 0 0 1 0 0 0 0 0 0 0 0 Epicrates cenchria 0 0 0 0 0 0 0 0 0 0 0 0 Epicrates crassus 0 0 0 0 0 0 1 0 0 0 0 0 Epictia aff. tenella 0 0 0 0 0 0 0 0 0 0 0 0 Epictia albipuncta 0 0 0 0 0 0 0 0 0 0 0 0 Epictia munoai 0 0 0 0 0 0 0 0 0 0 0 0 Epictia vellardi 0 0 0 0 0 0 0 0 0 0 0 0 Erythrolamprus aesculapii 0 0 0 0 0 0 1 1 0 0 0 0 Erythrolamprus albertguentheri 0 0 0 1 0 0 0 0 0 0 0 0 Erythrolamprus almadensis 0 0 0 0 0 0 1 0 0 0 0 0 Erythrolamprus frenatus 0 0 0 0 0 0 1 0 0 0 0 0 Erythrolamprus jaegeri 0 0 0 0 0 0 0 0 0 0 0 0 Erythrolamprus maryellenae 0 0 0 0 0 0 0 0 0 0 0 0 Erythrolamprus miliaris 0 0 0 0 0 0 0 0 0 0 0 0 Erythrolamprus poecilogyrus 0 0 0 1 0 1 1 1 0 0 0 0 Erythrolamprus reginae 0 0 0 0 0 0 0 0 0 1 0 0 Erythrolamprus sagittifer 1 1 1 0 0 0 0 0 0 0 1 0 Erythrolamprus semiaureus 0 0 0 0 0 0 0 0 0 0 0 0 Erythrolamprus taeniogaster 0 0 0 0 0 0 0 0 0 0 0 0 Erythrolamprus typhlus 0 0 0 0 0 1 1 0 0 0 0 0 Eunectes murinus 0 0 0 0 0 1 1 0 0 0 0 0 Eunectes notaeus 0 0 0 0 1 1 1 1 0 0 0 0 Helicops angulatus 0 0 0 0 0 0 0 0 0 0 0 0 Helicops infrataeniatus 0 0 0 0 0 0 0 0 0 0 0 0 Helicops leopardinus 0 0 0 0 1 1 1 0 0 0 0 0 Helicops modestus 0 0 0 0 0 0 0 0 0 0 0 0 Helicops polylepis 0 0 0 0 0 0 0 0 0 0 0 0 Hydrodynastes bicinctus 0 0 0 0 0 0 0 0 0 0 0 0 Hydrodynastes gigas 0 0 0 0 1 1 0 0 0 0 0 0 Hydrops caesurus 0 0 0 0 0 0 1 0 0 0 0 0 Imantodes cenchoa 0 0 0 0 0 0 0 0 0 0 0 0 Leptodeira annulata 0 0 0 0 1 1 1 1 0 0 0 0 Leptophis ahaetulla 0 0 0 0 0 1 1 1 0 0 0 0 Liotyphlops beui 0 0 0 0 0 0 0 0 0 0 0 0
53
49 50 51 52* 53 54 55 56 57 58 59 60* Clelia clelia 0 0 0 0 0 0 1 0 0 0 0 0 Clelia langeri 0 0 0 0 0 0 0 0 0 0 0 0 Clelia plumbea 0 0 0 0 0 1 0 0 0 0 0 0 Corallus hortulanus 0 0 0 0 0 0 0 0 0 0 0 0 Crotalus durissus 0 1 0 0 0 1 1 0 0 0 1 0 Dipsas bucephala 0 0 0 0 0 0 0 0 0 0 0 0 Dipsas catesbyi 0 0 0 0 0 0 0 0 0 0 0 0 Dipsas indica 0 0 0 0 0 0 0 0 0 0 0 0 Drepanoides anomalus 0 0 0 0 0 0 0 0 0 0 0 0 Drymarchon corais 0 1 0 1 0 1 1 1 0 0 0 0 Drymoluber brazili 0 0 0 0 0 1 1 1 0 0 0 0 Epicrates alvarezi 0 1 0 0 0 0 0 0 0 0 0 0 Epicrates cenchria 0 0 0 0 0 0 0 0 0 0 0 0 Epicrates crassus 0 0 0 0 0 1 1 0 0 0 0 0 Epictia aff. tenella 0 0 0 0 0 0 0 0 0 0 0 0 Epictia albipuncta 0 0 0 0 0 0 0 0 0 0 0 0 Epictia munoai 0 0 0 0 0 0 0 0 0 0 0 0 Epictia vellardi 0 0 0 0 0 0 0 0 0 0 0 0 Erythrolamprus aesculapii 0 0 0 0 0 1 1 0 0 0 0 0 Erythrolamprus albertguentheri 0 1 0 0 0 0 0 0 0 0 0 0 Erythrolamprus almadensis 0 1 0 0 0 1 1 0 0 0 0 0 Erythrolamprus frenatus 0 0 0 0 0 0 0 1 0 0 0 0 Erythrolamprus jaegeri 0 0 0 0 0 0 0 1 0 0 0 0 Erythrolamprus maryellenae 0 0 0 0 0 0 0 0 0 0 0 0 Erythrolamprus miliaris 0 0 0 0 0 0 0 0 0 0 0 0 Erythrolamprus poecilogyrus 0 1 1 1 1 1 1 0 0 1 0 1 Erythrolamprus reginae 0 0 0 0 0 1 0 0 0 0 0 0 Erythrolamprus sagittifer 0 1 1 0 0 0 0 0 0 0 0 0 Erythrolamprus semiaureus 0 0 0 0 0 0 0 0 0 0 0 0 Erythrolamprus taeniogaster 0 0 0 0 0 0 0 0 0 0 0 0 Erythrolamprus typhlus 0 0 0 0 0 1 1 0 0 0 0 0 Eunectes murinus 0 0 0 0 0 1 1 0 0 0 0 0 Eunectes notaeus 0 0 0 1 1 1 1 0 0 0 0 0 Helicops angulatus 0 0 0 0 0 0 0 0 0 0 0 0 Helicops infrataeniatus 0 0 0 0 0 0 1 1 0 0 0 0 Helicops leopardinus 0 0 0 1 0 1 1 0 0 0 0 0 Helicops modestus 0 0 0 0 0 0 1 0 0 0 0 0 Helicops polylepis 0 0 0 0 0 0 0 0 0 0 0 0 Hydrodynastes bicinctus 0 0 0 0 0 1 1 0 0 0 0 0 Hydrodynastes gigas 0 0 0 1 0 1 1 0 0 0 0 1 Hydrops caesurus 0 0 0 0 0 1 1 0 0 0 0 0 Imantodes cenchoa 0 0 0 0 0 0 0 0 0 0 0 0 Leptodeira annulata 0 1 1 0 0 1 1 0 0 0 0 0 Leptophis ahaetulla 0 0 1 1 0 1 1 0 0 0 0 0 Liotyphlops beui 0 0 0 0 0 0 1 1 0 0 0 0
54
61* 62* 63* 64 65 66 67 68* 69* 70* 71 72 Clelia clelia 1 1 1 0 0 0 0 0 1 0 0 0 Clelia langeri 0 0 0 0 0 0 0 0 0 0 0 0 Clelia plumbea 1 0 1 0 0 0 0 0 0 0 0 0 Corallus hortulanus 1 0 0 0 0 0 0 0 0 0 0 0 Crotalus durissus 1 1 1 1 0 0 0 1 1 0 1 0 Dipsas bucephala 0 0 0 1 0 0 0 0 0 0 0 0 Dipsas catesbyi 0 0 0 0 0 0 0 0 0 0 0 0 Dipsas indica 0 0 0 0 0 0 0 0 0 0 0 0 Drepanoides anomalus 0 0 0 0 0 0 0 0 0 0 0 0 Drymarchon corais 1 0 1 1 0 0 0 0 0 0 1 0 Drymoluber brazili 0 0 0 0 0 0 0 0 0 0 0 0 Epicrates alvarezi 0 0 0 0 0 0 0 0 0 0 0 0 Epicrates cenchria 0 0 0 0 0 0 0 0 0 0 0 0 Epicrates crassus 1 0 1 1 0 0 0 0 0 0 0 0 Epictia aff. tenella 1 0 0 0 0 0 0 0 0 0 0 0 Epictia albipuncta 0 0 0 0 0 0 0 0 0 0 0 0 Epictia munoai 0 0 0 0 0 0 0 0 0 0 0 0 Epictia vellardi 0 0 0 0 0 0 0 0 0 0 0 0 Erythrolamprus aesculapii 0 0 1 0 0 0 0 0 0 0 1 0 Erythrolamprus albertguentheri 0 0 0 0 0 0 0 0 0 0 0 0 Erythrolamprus almadensis 0 0 1 0 0 0 0 0 1 0 1 0 Erythrolamprus frenatus 0 1 0 0 0 0 0 0 0 0 0 0 Erythrolamprus jaegeri 1 0 0 0 0 0 0 1 1 0 0 0 Erythrolamprus maryellenae 0 0 0 0 0 0 0 0 0 0 0 0 Erythrolamprus miliaris 1 0 0 0 0 0 0 0 0 0 0 0 Erythrolamprus poecilogyrus 1 1 1 1 0 0 0 1 1 0 1 1 Erythrolamprus reginae 1 1 0 0 0 0 0 1 0 0 0 1 Erythrolamprus sagittifer 0 0 0 0 0 0 0 0 0 0 0 0 Erythrolamprus semiaureus 0 0 0 0 0 0 0 0 0 0 0 0 Erythrolamprus taeniogaster 0 0 0 0 0 0 0 0 0 0 0 0 Erythrolamprus typhlus 1 1 1 1 0 0 0 1 1 0 0 0 Eunectes murinus 1 1 1 0 0 0 0 1 0 0 1 0 Eunectes notaeus 1 1 0 1 0 0 0 1 1 0 0 0 Helicops angulatus 1 0 0 0 0 0 0 0 0 0 0 0 Helicops infrataeniatus 0 0 0 0 0 0 0 0 0 0 0 0 Helicops leopardinus 1 1 1 0 0 0 0 1 1 0 1 0 Helicops modestus 0 0 0 0 0 0 0 0 0 0 0 0 Helicops polylepis 0 0 0 0 0 0 0 0 0 0 1 1 Hydrodynastes bicinctus 0 0 0 0 0 0 0 0 0 0 1 0 Hydrodynastes gigas 1 1 1 0 0 0 0 1 1 0 1 0 Hydrops caesurus 1 0 1 0 0 0 0 1 0 0 0 0 Imantodes cenchoa 1 0 0 1 0 0 0 0 0 0 0 0 Leptodeira annulata 1 1 1 1 0 0 0 1 1 1 1 0 Leptophis ahaetulla 1 1 0 0 0 0 0 1 1 0 0 0 Liotyphlops beui 1 0 0 0 0 0 0 1 0 0 0 0
55
73* 74* 75 76 77 78* 79* 80* 81 82 83 84 Clelia clelia 0 0 1 0 0 1 0 0 0 0 0 0 Clelia langeri 0 0 0 0 0 0 0 0 0 0 0 0 Clelia plumbea 0 1 1 0 0 0 1 0 0 1 1 0 Corallus hortulanus 0 0 0 0 0 0 0 0 0 0 1 0 Crotalus durissus 1 0 1 0 0 1 1 0 1 1 1 1 Dipsas bucephala 0 0 0 0 0 0 0 0 0 1 0 0 Dipsas catesbyi 0 0 0 0 0 0 0 0 0 1 0 0 Dipsas indica 0 0 0 0 0 0 0 0 0 1 0 1 Drepanoides anomalus 0 0 0 0 0 0 0 0 0 1 0 0 Drymarchon corais 1 0 0 0 1 1 1 0 0 1 1 1 Drymoluber brazili 0 0 0 0 0 0 0 0 0 0 0 0 Epicrates alvarezi 0 0 0 0 0 0 0 0 0 0 0 0 Epicrates cenchria 0 0 1 0 0 0 0 0 0 0 0 1 Epicrates crassus 1 0 1 0 0 1 1 0 0 0 1 1 Epictia aff. tenella 0 0 0 0 0 0 0 0 0 1 1 1 Epictia albipuncta 0 0 0 0 0 0 0 0 0 0 0 0 Epictia munoai 0 0 0 0 0 0 0 0 0 0 0 0 Epictia vellardi 1 0 0 0 0 0 0 0 0 0 0 0 Erythrolamprus aesculapii 0 0 1 0 0 0 1 0 0 1 0 1 Erythrolamprus albertguentheri 0 0 0 0 0 0 0 0 0 0 0 0 Erythrolamprus almadensis 1 1 0 0 0 1 1 0 0 1 1 1 Erythrolamprus frenatus 0 0 1 0 0 0 0 0 0 0 0 0 Erythrolamprus jaegeri 1 0 0 0 0 0 0 0 0 0 0 0 Erythrolamprus maryellenae 0 0 0 0 0 0 0 0 0 0 0 0 Erythrolamprus miliaris 0 0 0 0 0 0 0 0 0 1 0 0 Erythrolamprus poecilogyrus 1 0 1 0 1 1 1 0 1 0 0 1 Erythrolamprus reginae 0 0 1 1 1 1 1 0 1 1 1 1 Erythrolamprus sagittifer 0 0 0 0 0 0 0 0 0 0 0 0 Erythrolamprus semiaureus 0 0 0 0 0 0 0 0 0 0 0 0 Erythrolamprus taeniogaster 0 0 0 0 0 0 0 0 0 1 1 1 Erythrolamprus typhlus 1 0 0 0 1 0 1 0 0 0 0 1 Eunectes murinus 0 0 1 0 0 0 0 0 0 1 1 1 Eunectes notaeus 1 1 0 0 0 1 1 0 0 0 0 0 Helicops angulatus 0 0 1 0 0 1 0 0 1 1 1 1 Helicops infrataeniatus 0 0 1 0 0 0 0 0 0 0 0 0 Helicops leopardinus 1 0 0 0 1 1 1 1 0 0 1 1 Helicops modestus 0 0 0 0 0 0 0 0 0 0 0 0 Helicops polylepis 0 0 0 1 0 1 0 0 0 0 0 0 Hydrodynastes bicinctus 0 0 0 0 0 0 0 0 0 0 0 0 Hydrodynastes gigas 1 0 0 1 0 1 1 0 0 0 0 1 Hydrops caesurus 1 0 0 0 0 0 1 0 0 0 0 0 Imantodes cenchoa 0 0 0 0 0 0 0 0 0 0 0 1 Leptodeira annulata 0 0 0 1 1 1 1 1 0 1 1 1 Leptophis ahaetulla 1 0 1 0 1 1 1 1 0 1 1 1 Liotyphlops beui 0 0 1 0 0 0 0 0 0 0 1 0
56
85 86 87 88 89 Clelia clelia 0 0 0 0 0 Clelia langeri 0 0 0 0 0 Clelia plumbea 0 0 0 0 1 Corallus hortulanus 1 0 1 0 1 Crotalus durissus 1 0 0 1 1 Dipsas bucephala 0 0 0 0 1 Dipsas catesbyi 0 0 0 0 0 Dipsas indica 0 0 0 0 1 Drepanoides anomalus 0 0 0 0 0 Drymarchon corais 0 0 0 1 1 Drymoluber brazili 1 0 0 0 1 Epicrates alvarezi 0 0 0 0 0 Epicrates cenchria 0 0 0 0 0 Epicrates crassus 1 0 0 1 1 Epictia aff. tenella 0 0 0 1 1 Epictia albipuncta 0 0 0 0 0 Epictia munoai 0 0 0 0 0 Epictia vellardi 0 0 0 0 0 Erythrolamprus aesculapii 0 0 0 1 1 Erythrolamprus albertguentheri 0 0 0 0 0 Erythrolamprus almadensis 1 0 0 1 1 Erythrolamprus frenatus 0 0 0 0 0 Erythrolamprus jaegeri 0 0 0 0 0 Erythrolamprus maryellenae 1 0 0 0 0 Erythrolamprus miliaris 0 0 0 0 0 Erythrolamprus poecilogyrus 1 1 0 1 1 Erythrolamprus reginae 1 1 1 0 1 Erythrolamprus sagittifer 0 0 0 0 0 Erythrolamprus semiaureus 0 0 0 0 0 Erythrolamprus taeniogaster 0 0 0 0 1 Erythrolamprus typhlus 0 0 1 1 1 Eunectes murinus 1 0 1 0 1 Eunectes notaeus 0 0 0 0 0 Helicops angulatus 1 0 0 0 1 Helicops infrataeniatus 0 0 0 0 0 Helicops leopardinus 1 0 0 1 0 Helicops modestus 0 0 0 0 0 Helicops polylepis 0 0 0 0 1 Hydrodynastes bicinctus 0 1 1 0 1 Hydrodynastes gigas 1 0 1 0 0 Hydrops caesurus 0 0 0 0 0 Imantodes cenchoa 1 0 0 0 1 Leptodeira annulata 1 0 0 1 1 Leptophis ahaetulla 1 0 1 0 1 Liotyphlops beui 1 0 0 0 0
57
1 2 3 4 5 6 7 8 9 10 11 12 Liotyphlops ternetzii 0 0 0 0 0 0 0 0 0 1 0 0 Lygophis dilepis 0 0 0 1 0 1 0 0 0 1 0 0 Lygophis flavifrenatus 0 0 0 0 0 0 0 0 0 0 0 0 Lygophis meridionalis 0 0 0 1 0 1 0 0 0 1 0 0 Lygophis paucidens 0 0 0 0 0 0 0 0 0 0 0 0 Mastigodryas bifossatus 0 0 0 0 0 0 1 0 0 1 1 0 Mastigodryas boddaerti 0 0 0 0 0 0 0 0 0 0 0 0 Micrurus altirostris 0 0 0 0 1 1 0 0 0 0 0 0 Micrurus annellatus 0 0 0 0 0 0 0 0 0 0 0 0 Micrurus baliocoryphus 0 0 0 1 0 0 0 0 0 1 0 0 Micrurus corallinus 0 0 0 0 0 0 1 0 0 0 0 0 Micrurus frontalis 0 0 0 0 0 1 0 0 0 1 1 0 Micrurus lemniscatus 0 0 0 0 0 0 0 0 0 0 0 0 Micrurus paraensis 0 0 0 0 0 0 0 0 0 0 0 0 Micrurus pyrrhocryptus 0 0 0 0 0 0 0 0 0 0 0 0 Micrurus silviae 0 0 0 0 0 0 1 0 0 0 0 0 Micrurus surinamensis 0 0 0 0 0 0 0 0 0 0 0 0 Micrurus tricolor 0 0 0 0 0 0 0 0 0 0 0 0 Mussurana bicolor 1 1 1 1 1 0 0 1 0 1 0 1 Mussurana quimi 0 0 0 0 0 0 0 0 0 0 0 0 Oxybelis aeneus 0 0 0 0 0 0 0 0 0 0 0 0 Oxybelis fulgidus 0 0 0 0 0 0 0 0 0 0 0 0 Oxyrhopus guibei 0 0 0 0 0 1 1 0 0 1 0 0 Oxyrhopus melanogenys 0 0 0 0 0 0 0 0 0 0 0 0 Oxyrhopus petolarius 0 0 0 0 0 0 0 0 0 0 0 0 Oxyrhopus rhombifer 0 0 0 0 0 0 0 0 0 0 0 0 Oxyrhopus trigeminus 0 0 0 0 0 0 0 0 0 0 0 0 Paraphimophis rustica 0 0 0 0 0 0 0 0 0 0 0 0 Phalotris matogrossensis 0 0 0 0 0 0 0 0 0 1 1 0 Phalotris mertensi 0 0 0 0 0 0 0 0 0 0 0 0 Phalotris nasutus 0 0 0 0 0 0 0 0 0 0 0 0 Phalotris nigrilatus 0 0 0 0 0 0 0 0 0 0 0 0 Phalotris tricolor 0 0 0 0 0 0 0 0 0 1 1 0 Philodryas aestiva 0 0 0 0 0 0 1 0 0 0 1 0 Philodryas agassizii 0 0 0 0 0 0 0 0 0 0 0 0 Philodryas baroni 0 0 0 0 0 0 0 0 0 0 0 0 Philodryas livida 0 0 0 0 0 0 0 0 0 0 0 0 Philodryas mattogrossensis 0 0 0 0 0 0 0 0 0 0 0 0 Philodryas nattereri 0 0 0 0 0 0 0 0 0 0 0 0 Philodryas olfersii 0 0 0 0 1 1 1 0 0 1 0 0 Philodryas patagoniensis 0 0 0 1 1 0 0 0 0 1 1 0 Philodryas psammophidea 0 0 0 0 0 0 0 0 0 0 0 0 Philodryas varia 0 0 0 0 0 0 0 0 0 0 0 0 Philodryas viridissima 0 0 0 0 0 0 0 0 0 0 0 0 Phimophis guerini 0 0 0 0 1 0 0 0 0 1 0 0
58
13 14 15 16 17 18 19 20 21 22 23 24 Liotyphlops ternetzii 0 1 0 1 0 0 0 0 0 0 0 0 Lygophis dilepis 0 1 1 0 0 0 0 0 1 0 0 0 Lygophis flavifrenatus 0 0 0 1 0 0 0 0 0 0 0 0 Lygophis meridionalis 0 0 1 1 0 0 0 0 0 0 0 1 Lygophis paucidens 0 0 0 0 0 0 0 0 0 0 0 0 Mastigodryas bifossatus 0 1 1 1 1 0 0 0 1 0 1 0 Mastigodryas boddaerti 0 0 0 0 0 0 0 0 0 0 0 0 Micrurus altirostris 0 0 0 0 1 0 0 0 0 0 0 0 Micrurus annellatus 0 0 0 0 0 0 0 0 0 0 0 0 Micrurus baliocoryphus 0 1 1 0 0 0 0 1 1 0 0 0 Micrurus corallinus 0 0 0 0 0 0 0 0 0 0 0 0 Micrurus frontalis 0 0 0 1 0 0 0 0 0 0 0 1 Micrurus lemniscatus 0 0 0 0 0 0 0 0 0 0 0 0 Micrurus paraensis 0 0 0 0 0 0 0 0 0 0 0 0 Micrurus pyrrhocryptus 0 0 0 0 0 0 0 0 0 0 0 0 Micrurus silviae 0 0 0 0 0 0 0 0 0 0 0 0 Micrurus surinamensis 0 0 0 0 0 0 0 0 0 0 0 0 Micrurus tricolor 0 0 0 0 0 0 0 0 0 0 0 0 Mussurana bicolor 0 1 1 1 0 0 0 0 1 1 1 0 Mussurana quimi 0 0 0 0 0 0 0 0 0 0 0 0 Oxybelis aeneus 0 0 0 0 0 0 0 0 0 0 0 0 Oxybelis fulgidus 0 0 0 0 0 0 0 0 0 0 0 0 Oxyrhopus guibei 0 1 0 1 1 0 0 0 0 0 1 0 Oxyrhopus melanogenys 0 0 0 0 0 0 0 0 0 0 0 0 Oxyrhopus petolarius 0 0 0 0 0 0 0 0 0 0 0 0 Oxyrhopus rhombifer 0 0 0 0 0 0 0 0 1 1 0 0 Oxyrhopus trigeminus 0 0 0 0 0 0 0 0 0 0 0 0 Paraphimophis rustica 0 0 0 0 0 0 0 0 0 0 0 0 Phalotris matogrossensis 0 0 0 0 0 0 0 0 0 0 0 0 Phalotris mertensi 0 0 0 0 0 0 0 0 0 0 0 0 Phalotris nasutus 0 0 0 0 0 0 0 0 0 0 0 0 Phalotris nigrilatus 0 0 0 1 0 0 0 0 0 0 0 1 Phalotris tricolor 0 0 1 0 0 0 0 0 0 0 0 0 Philodryas aestiva 0 0 0 1 1 0 0 0 0 0 0 0 Philodryas agassizii 0 0 0 0 0 0 0 0 0 0 0 0 Philodryas baroni 0 0 0 0 0 0 0 0 0 0 0 0 Philodryas livida 0 0 0 0 0 0 0 0 0 0 0 0 Philodryas mattogrossensis 0 0 0 0 0 0 0 0 1 1 0 0 Philodryas nattereri 0 0 0 0 0 0 0 0 0 0 0 1 Philodryas olfersii 0 0 1 1 0 0 0 0 0 0 0 1 Philodryas patagoniensis 0 1 1 1 1 0 0 0 1 1 0 1 Philodryas psammophidea 0 0 0 0 0 0 0 0 0 0 0 0 Philodryas varia 0 0 0 0 0 0 0 0 0 0 0 0 Philodryas viridissima 0 0 0 0 0 0 0 0 0 0 0 0 Phimophis guerini 0 0 0 0 0 0 0 0 0 0 0 1
59
25 26 27 28 29 30 31 32* 33 34 35 36 Liotyphlops ternetzii 0 0 0 0 0 0 0 0 1 0 0 0 Lygophis dilepis 0 0 0 0 1 1 0 0 0 0 0 0 Lygophis flavifrenatus 0 0 0 0 0 0 0 0 1 0 0 0 Lygophis meridionalis 0 0 0 0 1 0 0 0 1 1 0 0 Lygophis paucidens 0 0 0 0 0 0 0 0 0 0 0 0 Mastigodryas bifossatus 0 0 0 0 0 1 0 0 1 0 0 1 Mastigodryas boddaerti 0 0 0 0 0 0 0 0 0 0 0 0 Micrurus altirostris 0 0 0 0 0 0 0 0 0 0 0 0 Micrurus annellatus 0 0 0 0 0 0 0 0 0 0 0 0 Micrurus baliocoryphus 0 0 0 0 0 0 0 0 0 0 0 0 Micrurus corallinus 0 0 0 0 0 0 0 0 0 0 0 0 Micrurus frontalis 0 0 0 0 0 0 0 0 1 1 0 1 Micrurus lemniscatus 1 0 0 0 0 0 0 0 0 1 0 0 Micrurus paraensis 0 0 0 0 0 0 0 0 0 0 0 0 Micrurus pyrrhocryptus 0 0 1 0 0 0 0 0 0 0 0 0 Micrurus silviae 0 0 0 0 0 0 0 0 0 0 0 0 Micrurus surinamensis 0 0 0 0 0 0 0 0 0 0 0 0 Micrurus tricolor 0 0 0 0 0 0 0 1 0 0 0 0 Mussurana bicolor 0 0 0 0 0 1 0 0 0 0 0 0 Mussurana quimi 0 0 0 0 0 0 0 0 0 0 0 0 Oxybelis aeneus 0 0 0 0 0 0 0 0 0 0 0 0 Oxybelis fulgidus 0 0 0 0 0 0 0 0 0 0 0 0 Oxyrhopus guibei 0 0 0 0 0 0 0 0 1 1 0 0 Oxyrhopus melanogenys 0 0 0 0 0 0 0 0 0 0 0 0 Oxyrhopus petolarius 0 0 0 0 0 0 0 0 0 0 0 0 Oxyrhopus rhombifer 0 0 0 0 1 1 0 0 0 0 0 0 Oxyrhopus trigeminus 0 0 0 0 0 0 0 0 0 0 0 0 Paraphimophis rustica 0 0 1 0 0 0 0 0 0 0 0 0 Phalotris matogrossensis 0 0 0 0 0 0 0 0 1 0 0 0 Phalotris mertensi 0 0 0 0 0 0 0 0 0 0 0 0 Phalotris nasutus 0 0 0 0 0 0 0 0 0 0 0 0 Phalotris nigrilatus 0 0 0 0 0 0 0 0 0 0 0 0 Phalotris tricolor 0 0 0 0 1 1 0 0 0 0 0 0 Philodryas aestiva 0 0 0 0 0 0 0 0 0 0 0 0 Philodryas agassizii 0 0 0 0 0 0 0 0 0 1 0 0 Philodryas baroni 0 0 0 0 1 0 0 0 0 0 0 0 Philodryas livida 0 0 0 0 0 0 0 0 0 0 0 0 Philodryas mattogrossensis 1 0 1 0 1 1 0 0 1 0 0 1 Philodryas nattereri 0 0 0 0 0 0 0 0 0 0 0 0 Philodryas olfersii 0 0 0 0 0 1 0 1 1 0 0 0 Philodryas patagoniensis 0 0 0 0 0 1 0 1 1 1 0 0 Philodryas psammophidea 0 0 0 0 1 1 0 0 0 0 1 0 Philodryas varia 0 0 0 0 0 0 0 0 0 0 1 0 Philodryas viridissima 0 0 0 0 0 0 0 0 0 0 0 0 Phimophis guerini 0 0 0 0 0 0 0 0 0 0 0 0
60
37 38 39 40 41 42* 43 44 45 46 47 48 Liotyphlops ternetzii 0 0 0 0 0 0 0 0 0 0 0 0 Lygophis dilepis 0 0 0 0 0 1 1 0 0 0 0 0 Lygophis flavifrenatus 0 0 0 0 0 0 0 0 0 0 0 0 Lygophis meridionalis 0 0 0 0 0 0 1 0 0 0 0 0 Lygophis paucidens 0 0 0 0 0 0 0 0 0 0 0 0 Mastigodryas bifossatus 0 0 0 0 0 1 1 0 0 0 0 0 Mastigodryas boddaerti 0 0 0 0 0 0 0 0 0 0 0 0 Micrurus altirostris 0 0 0 0 0 0 0 0 0 0 0 0 Micrurus annellatus 0 0 0 0 0 0 0 0 0 0 1 0 Micrurus baliocoryphus 0 0 0 1 0 0 0 0 0 0 0 0 Micrurus corallinus 0 0 0 0 0 0 0 0 0 0 0 0 Micrurus frontalis 0 0 0 0 0 0 1 1 0 0 0 0 Micrurus lemniscatus 0 0 0 0 0 0 0 1 0 0 0 0 Micrurus paraensis 0 0 0 0 0 0 0 0 0 0 0 0 Micrurus pyrrhocryptus 0 1 0 0 0 0 0 0 0 0 0 0 Micrurus silviae 0 0 0 0 0 0 0 0 0 0 0 0 Micrurus surinamensis 0 0 0 0 0 0 0 0 0 0 0 0 Micrurus tricolor 0 0 0 0 1 1 1 0 0 0 0 0 Mussurana bicolor 0 0 1 0 0 1 1 0 0 0 0 0 Mussurana quimi 0 0 0 0 0 0 0 0 0 0 0 0 Oxybelis aeneus 0 0 0 0 0 0 0 0 0 0 0 0 Oxybelis fulgidus 0 0 0 0 0 0 0 0 0 0 0 0 Oxyrhopus guibei 0 0 0 0 1 0 1 0 0 0 0 0 Oxyrhopus melanogenys 0 0 0 0 0 0 0 0 0 0 0 0 Oxyrhopus petolarius 0 0 0 0 0 0 0 0 0 0 0 0 Oxyrhopus rhombifer 0 1 0 0 0 1 0 0 0 0 0 0 Oxyrhopus trigeminus 0 0 0 0 0 0 1 0 0 0 0 0 Paraphimophis rustica 0 0 0 0 0 0 0 0 0 0 0 0 Phalotris matogrossensis 0 0 0 0 0 0 1 1 0 0 0 0 Phalotris mertensi 0 0 0 0 0 0 0 0 0 0 0 0 Phalotris nasutus 0 0 0 0 0 0 0 0 0 0 0 0 Phalotris nigrilatus 0 0 0 0 0 0 0 0 0 0 0 0 Phalotris tricolor 0 0 0 0 0 0 0 0 0 0 0 0 Philodryas aestiva 0 0 0 0 0 0 0 0 0 0 0 0 Philodryas agassizii 0 0 0 0 0 0 0 0 0 0 0 0 Philodryas baroni 0 1 0 0 0 0 0 0 0 0 0 0 Philodryas livida 0 0 0 0 0 0 0 0 0 0 0 0 Philodryas mattogrossensis 1 0 0 0 0 1 1 0 0 0 0 0 Philodryas nattereri 0 0 0 0 0 0 0 0 0 0 0 0 Philodryas olfersii 0 0 0 0 0 0 1 1 0 0 0 0 Philodryas patagoniensis 0 0 0 0 0 1 0 0 0 0 0 0 Philodryas psammophidea 1 1 0 0 0 0 1 0 0 0 0 0 Philodryas varia 0 0 0 0 0 0 0 0 0 1 0 0 Philodryas viridissima 0 0 0 0 0 0 0 0 0 0 0 0 Phimophis guerini 0 0 0 0 0 1 0 1 0 0 0 0
61
49 50 51 52* 53 54 55 56 57 58 59 60* Liotyphlops ternetzii 0 0 0 0 0 0 0 1 0 0 0 0 Lygophis dilepis 0 0 0 1 0 1 0 0 0 0 0 0 Lygophis flavifrenatus 0 0 0 0 0 1 0 0 0 0 0 0 Lygophis meridionalis 0 0 0 0 0 1 1 1 0 0 0 0 Lygophis paucidens 0 0 0 0 0 0 0 0 0 0 0 0 Mastigodryas bifossatus 0 0 0 0 0 1 1 1 0 0 0 0 Mastigodryas boddaerti 0 0 0 0 0 0 0 0 0 0 0 0 Micrurus altirostris 0 0 0 0 0 0 0 0 0 0 0 0 Micrurus annellatus 0 0 0 0 0 0 0 0 0 0 0 0 Micrurus baliocoryphus 0 0 0 0 0 0 0 0 0 0 0 0 Micrurus corallinus 0 0 0 0 0 0 0 0 0 0 0 0 Micrurus frontalis 0 0 0 0 0 1 1 1 0 0 0 0 Micrurus lemniscatus 0 0 0 0 0 1 0 1 0 0 0 0 Micrurus paraensis 0 0 0 0 0 0 0 0 0 0 0 0 Micrurus pyrrhocryptus 1 0 0 1 0 0 0 0 0 0 0 0 Micrurus silviae 0 0 0 0 0 0 0 0 0 0 0 0 Micrurus surinamensis 0 0 0 0 0 0 0 0 0 0 0 0 Micrurus tricolor 0 0 0 0 0 1 1 0 0 0 0 0 Mussurana bicolor 0 0 1 1 1 1 1 0 0 0 0 0 Mussurana quimi 0 0 0 0 0 0 0 0 0 0 0 0 Oxybelis aeneus 0 0 0 0 0 1 1 0 0 0 0 0 Oxybelis fulgidus 0 0 0 0 0 0 0 0 0 0 0 0 Oxyrhopus guibei 0 0 0 0 0 1 1 1 0 0 0 0 Oxyrhopus melanogenys 0 0 0 0 0 0 0 0 0 0 0 0 Oxyrhopus petolarius 0 0 0 0 0 1 1 0 0 0 0 0 Oxyrhopus rhombifer 0 1 0 0 0 1 1 0 0 0 0 1 Oxyrhopus trigeminus 0 0 0 0 0 1 1 0 0 0 0 0 Paraphimophis rustica 0 0 0 0 0 0 0 0 0 0 0 0 Phalotris matogrossensis 0 0 0 0 0 1 1 0 0 0 0 0 Phalotris mertensi 0 0 0 0 0 0 0 0 0 0 0 0 Phalotris nasutus 0 0 0 0 0 0 1 0 0 0 0 0 Phalotris nigrilatus 0 0 0 0 0 0 0 0 0 0 0 0 Phalotris tricolor 0 0 0 0 0 0 0 0 0 0 0 0 Philodryas aestiva 0 0 0 0 0 1 1 1 0 0 0 0 Philodryas agassizii 0 0 0 0 0 0 0 1 0 0 0 0 Philodryas baroni 0 0 0 0 0 0 0 0 0 0 0 0 Philodryas livida 0 0 0 0 0 1 1 0 0 0 0 0 Philodryas mattogrossensis 0 1 1 0 0 1 1 1 0 0 0 0 Philodryas nattereri 0 0 0 0 0 1 1 1 0 0 0 0 Philodryas olfersii 0 0 0 1 0 1 1 1 0 0 0 0 Philodryas patagoniensis 0 0 0 0 1 1 1 0 0 0 0 1 Philodryas psammophidea 1 1 0 0 0 1 1 0 0 0 0 1 Philodryas varia 0 0 0 0 0 0 0 0 0 0 0 0 Philodryas viridissima 0 0 0 0 0 0 0 0 0 0 0 0 Phimophis guerini 0 0 0 0 0 1 1 1 0 0 0 0
62
61* 62* 63* 64 65 66 67 68* 69* 70* 71 72 Liotyphlops ternetzii 0 0 0 0 0 0 0 0 0 0 0 0 Lygophis dilepis 1 0 0 0 0 0 0 0 0 0 0 0 Lygophis flavifrenatus 0 0 0 0 0 0 0 0 0 0 0 0 Lygophis meridionalis 0 1 1 0 0 0 0 0 1 0 0 0 Lygophis paucidens 0 0 0 0 0 0 0 0 0 0 0 0 Mastigodryas bifossatus 1 1 1 1 0 0 0 1 1 0 1 0 Mastigodryas boddaerti 1 0 0 0 0 0 0 1 0 0 0 0 Micrurus altirostris 0 0 0 0 0 0 0 0 0 0 0 0 Micrurus annellatus 0 0 0 0 0 0 0 0 0 0 0 0 Micrurus baliocoryphus 0 0 0 0 0 0 0 0 0 0 0 0 Micrurus corallinus 0 0 0 0 0 0 0 0 0 0 0 0 Micrurus frontalis 0 1 0 0 0 0 0 0 1 0 1 1 Micrurus lemniscatus 0 0 0 0 0 0 0 0 0 0 1 0 Micrurus paraensis 0 0 0 0 0 0 0 0 0 0 0 0 Micrurus pyrrhocryptus 1 0 0 0 0 0 0 0 0 0 0 0 Micrurus silviae 0 0 0 0 0 0 0 0 0 0 0 0 Micrurus surinamensis 0 0 0 0 0 0 0 0 0 0 0 0 Micrurus tricolor 1 0 0 0 0 0 0 1 0 0 0 0 Mussurana bicolor 1 1 1 0 0 0 0 1 1 0 0 0 Mussurana quimi 0 0 0 0 0 0 0 0 0 0 0 0 Oxybelis aeneus 1 1 1 0 0 0 0 0 0 1 0 0 Oxybelis fulgidus 0 0 0 0 0 0 0 0 0 0 0 0 Oxyrhopus guibei 0 0 1 1 0 0 0 0 0 0 1 1 Oxyrhopus melanogenys 0 0 0 0 0 0 0 0 0 0 0 0 Oxyrhopus petolarius 1 1 1 0 0 0 0 1 0 0 0 0 Oxyrhopus rhombifer 1 1 0 0 0 0 1 1 1 0 0 0 Oxyrhopus trigeminus 1 1 1 1 0 0 0 1 1 0 1 0 Paraphimophis rustica 0 0 0 0 0 0 0 0 0 0 0 0 Phalotris matogrossensis 1 0 1 0 0 0 0 1 1 0 0 0 Phalotris mertensi 1 0 0 0 0 0 0 0 0 0 0 0 Phalotris nasutus 0 1 1 0 0 0 0 0 1 0 0 1 Phalotris nigrilatus 0 0 0 0 0 0 0 0 0 0 0 0 Phalotris tricolor 1 0 0 0 0 0 0 1 0 0 0 0 Philodryas aestiva 1 1 0 0 0 0 0 0 0 0 0 0 Philodryas agassizii 0 0 0 0 0 0 0 0 0 0 0 0 Philodryas baroni 0 0 0 0 0 0 0 0 0 0 0 0 Philodryas livida 1 0 0 0 0 0 0 0 0 1 0 0 Philodryas mattogrossensis 1 1 1 1 0 0 0 1 0 0 1 0 Philodryas nattereri 0 1 0 0 0 0 0 1 0 0 1 0 Philodryas olfersii 1 1 1 1 0 0 0 1 1 1 1 0 Philodryas patagoniensis 1 1 1 0 0 0 0 1 0 0 0 0 Philodryas psammophidea 0 0 1 0 0 0 0 0 0 0 0 0 Philodryas varia 0 0 0 0 0 0 0 0 0 0 0 0 Philodryas viridissima 0 0 0 0 0 0 0 0 0 0 0 0 Phimophis guerini 1 0 1 0 0 0 0 1 0 0 1 0
63
73* 74* 75 76 77 78* 79* 80* 81 82 83 84 Liotyphlops ternetzii 0 0 0 0 0 0 0 0 0 1 0 1 Lygophis dilepis 0 0 0 0 0 0 1 0 0 0 0 0 Lygophis flavifrenatus 0 0 0 0 0 0 0 0 0 0 0 0 Lygophis meridionalis 0 0 1 0 0 1 0 0 0 0 1 1 Lygophis paucidens 0 0 0 0 0 0 0 0 0 0 0 1 Mastigodryas bifossatus 0 0 1 0 0 1 1 0 0 1 1 1 Mastigodryas boddaerti 1 0 0 0 1 1 1 0 0 0 1 1 Micrurus altirostris 0 0 0 0 0 0 0 0 0 0 0 0 Micrurus annellatus 0 0 0 0 0 0 0 0 0 0 0 0 Micrurus baliocoryphus 0 0 0 0 0 0 0 0 0 0 0 0 Micrurus corallinus 0 0 0 0 0 0 0 0 0 0 0 0 Micrurus frontalis 0 0 1 1 0 0 0 0 0 0 1 1 Micrurus lemniscatus 0 0 1 0 0 0 1 0 0 0 0 0 Micrurus paraensis 0 0 0 0 0 0 0 0 0 1 0 0 Micrurus pyrrhocryptus 1 0 0 0 0 0 0 0 0 0 0 0 Micrurus silviae 0 0 0 0 0 0 0 0 0 0 0 0 Micrurus surinamensis 0 0 0 0 0 0 0 0 0 0 0 0 Micrurus tricolor 0 0 0 0 0 1 1 1 0 0 0 1 Mussurana bicolor 0 0 0 0 1 1 1 0 0 0 0 1 Mussurana quimi 0 0 0 1 0 0 0 0 0 0 0 0 Oxybelis aeneus 1 0 0 0 0 0 0 0 0 0 0 0 Oxybelis fulgidus 0 0 0 0 0 0 0 0 0 0 1 0 Oxyrhopus guibei 0 0 0 0 0 0 1 1 0 0 0 1 Oxyrhopus melanogenys 0 0 0 0 0 0 0 0 0 0 0 0 Oxyrhopus petolarius 1 0 0 0 0 0 1 0 0 0 0 1 Oxyrhopus rhombifer 1 0 0 0 1 1 1 0 0 1 1 1 Oxyrhopus trigeminus 0 0 1 0 0 1 1 0 0 1 1 1 Paraphimophis rustica 0 0 0 0 0 0 0 0 0 0 0 0 Phalotris matogrossensis 1 0 0 0 1 1 1 1 1 0 1 1 Phalotris mertensi 0 0 0 0 0 0 0 0 0 0 0 0 Phalotris nasutus 0 0 1 0 0 0 0 0 0 1 0 0 Phalotris nigrilatus 0 0 0 0 0 0 0 0 0 0 0 0 Phalotris tricolor 0 0 0 0 0 0 0 0 0 0 0 0 Philodryas aestiva 0 0 0 0 0 0 0 0 0 0 0 0 Philodryas agassizii 0 0 0 0 0 1 0 0 0 0 1 1 Philodryas baroni 0 0 0 0 0 0 0 0 0 0 0 0 Philodryas livida 0 0 0 0 0 0 0 0 0 0 0 0 Philodryas mattogrossensis 0 0 1 0 0 1 1 0 1 0 0 0 Philodryas nattereri 1 0 1 0 0 0 1 1 0 1 1 1 Philodryas olfersii 1 0 1 0 0 0 1 0 1 1 1 1 Philodryas patagoniensis 1 0 1 0 0 0 1 0 0 0 0 1 Philodryas psammophidea 0 0 0 0 0 0 0 0 0 0 0 1 Philodryas varia 0 0 0 0 0 0 0 0 0 0 0 0 Philodryas viridissima 0 0 0 0 0 1 0 0 0 1 0 0 Phimophis guerini 0 0 0 0 0 0 0 0 0 0 0 1
64
85 86 87 88 89 Liotyphlops ternetzii 1 0 1 0 0 Lygophis dilepis 1 0 0 0 0 Lygophis flavifrenatus 0 0 0 0 0 Lygophis meridionalis 0 0 0 1 1 Lygophis paucidens 1 0 0 0 1 Mastigodryas bifossatus 1 1 0 1 1 Mastigodryas boddaerti 0 0 0 1 1 Micrurus altirostris 0 0 0 0 0 Micrurus annellatus 0 0 0 0 0 Micrurus baliocoryphus 0 0 0 0 0 Micrurus corallinus 0 0 0 0 0 Micrurus frontalis 1 0 0 1 1 Micrurus lemniscatus 0 0 0 0 0 Micrurus paraensis 0 0 1 0 0 Micrurus pyrrhocryptus 0 0 0 0 0 Micrurus silviae 0 0 0 0 0 Micrurus surinamensis 0 0 1 1 1 Micrurus tricolor 1 0 0 0 0 Mussurana bicolor 0 0 0 0 0 Mussurana quimi 0 0 0 0 0 Oxybelis aeneus 1 0 0 1 1 Oxybelis fulgidus 0 0 1 0 0 Oxyrhopus guibei 1 0 0 0 1 Oxyrhopus melanogenys 0 0 1 0 0 Oxyrhopus petolarius 0 0 0 0 1 Oxyrhopus rhombifer 1 0 0 1 1 Oxyrhopus trigeminus 1 0 0 1 1 Paraphimophis rustica 0 0 0 0 0 Phalotris matogrossensis 0 0 0 0 0 Phalotris mertensi 0 0 0 0 0 Phalotris nasutus 0 0 0 0 0 Phalotris nigrilatus 0 0 0 0 0 Phalotris tricolor 0 0 0 0 0 Philodryas aestiva 0 0 0 0 0 Philodryas agassizii 1 0 0 0 0 Philodryas baroni 0 0 0 0 0 Philodryas livida 0 0 0 0 0 Philodryas mattogrossensis 0 0 0 0 0 Philodryas nattereri 1 1 0 1 1 Philodryas olfersii 1 0 0 0 1 Philodryas patagoniensis 1 1 0 0 1 Philodryas psammophidea 1 0 0 0 1 Philodryas varia 0 0 0 0 0 Philodryas viridissima 0 0 0 0 0 Phimophis guerini 1 0 0 1 1
65
1 2 3 4 5 6 7 8 9 10 11 12 Phimophis vittatus 0 0 0 0 0 0 0 0 0 0 0 0 Phrynonax poecilonotus 0 0 0 0 0 0 0 0 0 0 0 0 Pseudoboa coronata 0 0 0 0 0 0 0 0 0 0 0 0 Pseudoboa nigra 0 0 0 0 0 0 0 0 0 0 0 0 Pseudoeryx plicatilis 0 0 0 1 0 1 0 0 0 0 0 0 Psomophis genimaculatus 0 0 0 0 0 0 0 0 0 0 0 0 Psomophis obtusus 0 0 0 1 0 0 0 0 0 1 0 0 Rena unguirostris 0 0 0 0 0 0 0 0 0 0 0 0 Rhachidelus brazili 0 0 0 0 0 0 0 0 0 0 0 0 Sibynomorphus lavillai 0 0 0 0 0 0 0 0 0 0 0 0 Sibynomorphus mikanii 0 0 0 0 0 0 0 0 0 0 0 0 Sibynomorphus turgidus 0 0 0 0 0 0 0 0 0 1 1 0 Sibynomorphus ventrimaculatus 0 0 0 0 0 0 0 0 0 1 0 0 Simophis rhinostoma 0 0 0 0 0 0 0 0 0 0 1 0 Siphlophis compressus 0 0 0 0 0 0 0 0 0 0 0 0 Spilotes pullatus 0 0 0 0 0 1 1 0 0 0 0 0 Spilotes sulphureus 0 0 0 0 0 0 0 0 0 0 0 0 Tachymenis peruviana 0 0 0 0 0 0 0 0 0 0 0 0 Taeniophallus occipitalis 0 0 0 1 0 0 0 0 0 1 0 0 Tantilla melanocephala 0 0 0 0 0 0 0 0 0 1 0 0 Thamnodynastes chaquensis 0 0 0 0 1 0 0 0 0 1 0 0 Thamnodynastes hypoconia 0 0 0 0 1 0 1 0 0 1 0 0 Thamnodynastes lanei 0 0 0 0 0 0 0 0 0 0 0 0 Thamnodynastes rutilus 0 0 0 0 0 0 0 0 0 0 0 0 Thamnodynastes strigatus 0 0 0 0 1 0 0 0 0 0 0 0 Trilepida brasiliensis 0 0 0 0 0 0 0 0 0 0 0 0 Trilepida koppesi 0 0 0 0 0 0 0 0 0 0 0 0 Typhlops brongersmianus 0 0 0 0 0 0 0 0 0 1 1 0 Typhlops reticulatus 0 0 0 0 0 0 0 0 0 0 0 0 Xenodon dorbignyi 0 0 0 0 0 0 0 0 0 1 0 0 Xenodon histricus 0 0 0 0 0 0 0 0 0 0 0 0 Xenodon matogrossensis 0 0 0 0 0 0 0 0 0 0 0 0 Xenodon merremii 0 0 0 1 1 1 1 0 0 1 1 0 Xenodon nattereri 0 0 0 0 0 0 0 0 0 0 0 0 Xenodon pulcher 0 0 0 0 0 0 0 0 0 0 0 0 Xenodon rhabdocephalus 0 0 0 0 0 0 0 0 0 0 0 0 Xenodon semicinctus 0 0 0 0 0 0 0 0 0 0 0 0 Xenodon severus 0 0 0 0 0 0 0 0 0 0 0 0 Xenopholis undulatus 0 0 0 0 0 0 0 0 0 1 0 0 Xenopholis werdingorum 0 0 0 0 0 0 0 0 0 0 0 0
66
13 14 15 16 17 18 19 20 21 22 23 24 Phimophis vittatus 0 0 0 0 0 0 1 0 0 0 0 0 Phrynonax poecilonotus 0 0 0 0 0 0 0 0 0 0 0 0 Pseudoboa coronata 0 0 0 0 0 0 0 0 0 0 0 0 Pseudoboa nigra 0 0 0 0 0 0 0 0 0 0 0 0 Pseudoeryx plicatilis 0 0 0 0 0 0 0 0 0 0 0 0 Psomophis genimaculatus 0 0 0 0 0 0 0 0 0 0 0 0 Psomophis obtusus 0 0 0 0 0 0 0 0 0 0 0 0 Rena unguirostris 0 0 0 0 0 0 0 0 0 0 0 0 Rhachidelus brazili 0 0 0 1 0 0 0 0 0 0 0 0 Sibynomorphus lavillai 0 0 0 0 0 0 0 0 0 0 0 0 Sibynomorphus mikanii 0 0 0 0 0 0 0 0 0 0 0 0 Sibynomorphus turgidus 0 0 0 1 0 0 0 0 0 1 0 1 Sibynomorphus ventrimaculatus 0 0 0 0 0 0 0 0 0 0 0 0 Simophis rhinostoma 0 0 0 0 0 0 0 0 0 0 0 0 Siphlophis compressus 0 0 0 0 0 0 0 0 0 0 0 0 Spilotes pullatus 1 0 0 1 0 0 0 0 0 0 0 0 Spilotes sulphureus 0 0 0 0 0 0 0 0 0 0 0 0 Tachymenis peruviana 0 0 0 0 0 0 0 0 0 0 0 0 Taeniophallus occipitalis 0 0 0 0 0 0 0 0 0 0 0 1 Tantilla melanocephala 0 0 0 1 0 0 0 1 0 0 0 0 Thamnodynastes chaquensis 0 0 0 0 0 0 0 0 0 0 0 0 Thamnodynastes hypoconia 0 1 1 0 0 0 0 0 0 1 1 0 Thamnodynastes lanei 0 1 0 1 0 0 0 0 0 0 0 0 Thamnodynastes rutilus 0 0 0 0 0 0 0 0 0 0 0 0 Thamnodynastes strigatus 0 0 0 0 0 0 0 0 0 0 0 0 Trilepida brasiliensis 0 0 0 0 0 0 0 0 0 0 0 0 Trilepida koppesi 0 0 0 0 0 0 0 0 0 0 0 0 Typhlops brongersmianus 0 1 0 1 1 0 0 0 0 0 0 0 Typhlops reticulatus 0 0 0 0 0 0 0 0 0 0 0 0 Xenodon dorbignyi 0 0 0 0 0 0 0 0 0 0 0 0 Xenodon histricus 0 0 0 1 0 0 0 0 0 0 0 0 Xenodon matogrossensis 0 0 0 0 0 0 0 0 0 0 0 0 Xenodon merremii 0 0 0 1 0 0 0 0 1 0 1 1 Xenodon nattereri 0 0 0 0 0 0 0 0 0 0 0 0 Xenodon pulcher 0 0 0 0 0 0 1 0 1 0 0 0 Xenodon rhabdocephalus 0 0 0 0 0 0 0 0 0 0 0 0 Xenodon semicinctus 0 0 0 0 0 0 0 0 0 0 0 0 Xenodon severus 0 0 0 0 0 0 0 0 0 0 0 0 Xenopholis undulatus 0 0 0 1 0 0 0 0 0 0 0 0 Xenopholis werdingorum 0 0 0 0 0 0 0 0 0 0 0 0
67
25 26 27 28 29 30 31 32* 33 34 35 36 Phimophis vittatus 0 0 0 0 1 1 0 0 0 0 1 0 Phrynonax poecilonotus 0 0 0 0 0 0 0 0 0 0 0 0 Pseudoboa coronata 0 0 0 0 0 0 0 0 0 0 0 0 Pseudoboa nigra 0 0 0 1 0 0 1 0 1 0 0 0 Pseudoeryx plicatilis 0 0 0 0 0 0 0 0 0 0 0 0 Psomophis genimaculatus 0 0 0 0 1 1 0 0 0 0 0 0 Psomophis obtusus 0 0 0 0 0 0 0 0 0 0 0 0 Rena unguirostris 0 0 1 0 0 0 0 0 0 0 0 0 Rhachidelus brazili 0 0 0 0 0 0 0 0 0 0 0 0 Sibynomorphus lavillai 0 0 0 0 0 0 0 0 0 0 0 0 Sibynomorphus mikanii 0 0 0 0 0 0 0 0 0 0 0 0 Sibynomorphus turgidus 0 0 0 0 1 0 0 1 1 0 0 1 Sibynomorphus ventrimaculatus 0 0 0 0 0 0 0 0 1 1 0 0 Simophis rhinostoma 0 0 0 0 0 0 0 0 1 1 0 0 Siphlophis compressus 0 0 0 0 0 0 0 0 0 0 0 0 Spilotes pullatus 0 0 0 0 0 0 0 0 1 0 0 0 Spilotes sulphureus 0 0 0 0 0 0 0 0 0 0 0 0 Tachymenis peruviana 0 0 0 0 0 0 0 0 0 0 0 0 Taeniophallus occipitalis 0 0 0 0 1 0 0 0 1 0 0 0 Tantilla melanocephala 0 0 0 0 0 0 0 0 0 0 0 0 Thamnodynastes chaquensis 0 0 1 0 0 0 0 1 0 0 0 0 Thamnodynastes hypoconia 0 0 0 0 0 0 0 1 1 0 0 0 Thamnodynastes lanei 0 0 0 0 0 0 0 0 0 0 0 0 Thamnodynastes rutilus 0 0 0 0 0 0 0 0 0 0 0 0 Thamnodynastes strigatus 0 0 0 0 0 0 0 0 0 0 0 0 Trilepida brasiliensis 0 0 0 0 0 0 0 0 0 0 0 0 Trilepida koppesi 0 0 0 0 0 0 0 0 0 0 0 0 Typhlops brongersmianus 0 0 0 0 1 0 0 0 1 0 0 0 Typhlops reticulatus 0 0 0 0 0 0 0 0 0 0 0 0 Xenodon dorbignyi 0 0 0 0 0 0 0 0 0 0 0 0 Xenodon histricus 0 0 0 0 0 0 0 0 0 0 0 0 Xenodon matogrossensis 0 0 0 0 0 0 0 0 0 0 0 0 Xenodon merremii 0 0 0 0 1 1 0 0 1 0 1 1 Xenodon nattereri 0 0 0 0 0 0 0 0 0 1 0 0 Xenodon pulcher 0 0 0 0 1 1 0 0 0 0 0 0 Xenodon rhabdocephalus 0 0 0 0 0 0 0 0 0 0 0 0 Xenodon semicinctus 0 0 0 0 0 0 0 0 0 0 1 1 Xenodon severus 0 0 0 0 0 0 0 0 0 0 0 0 Xenopholis undulatus 0 0 0 0 0 0 0 0 0 0 0 0 Xenopholis werdingorum 0 0 0 0 0 0 0 0 0 0 0 0
68
37 38 39 40 41 42* 43 44 45 46 47 48 Phimophis vittatus 0 0 0 0 0 0 0 0 0 0 0 0 Phrynonax poecilonotus 0 0 0 0 0 0 0 0 0 0 0 0 Pseudoboa coronata 0 0 0 0 0 0 0 0 0 0 0 0 Pseudoboa nigra 0 1 0 0 0 1 1 0 0 0 0 0 Pseudoeryx plicatilis 0 0 0 0 0 0 0 0 0 0 0 0 Psomophis genimaculatus 0 0 0 0 0 1 0 1 0 0 0 0 Psomophis obtusus 0 0 0 0 0 0 0 0 0 0 0 0 Rena unguirostris 0 0 0 0 0 0 0 0 0 0 0 0 Rhachidelus brazili 0 0 0 0 0 0 1 0 0 0 0 0 Sibynomorphus lavillai 0 0 0 0 0 0 0 0 0 0 0 0 Sibynomorphus mikanii 0 0 0 0 0 1 1 0 0 0 0 0 Sibynomorphus turgidus 0 0 0 0 0 1 1 0 0 0 0 0 Sibynomorphus ventrimaculatus 0 0 0 0 0 0 0 0 0 0 0 0 Simophis rhinostoma 0 0 0 0 0 1 0 0 0 0 0 0 Siphlophis compressus 0 0 0 0 0 0 0 0 0 0 0 0 Spilotes pullatus 0 0 0 0 0 0 1 0 0 0 0 0 Spilotes sulphureus 0 0 0 0 0 0 0 0 0 0 0 0 Tachymenis peruviana 0 0 0 0 0 0 0 0 0 0 0 0 Taeniophallus occipitalis 0 0 0 1 0 1 1 0 0 0 0 0 Tantilla melanocephala 0 0 0 0 0 0 0 0 0 0 0 0 Thamnodynastes chaquensis 0 0 0 0 0 1 1 0 0 0 0 0 Thamnodynastes hypoconia 0 0 0 0 0 1 0 0 0 0 0 0 Thamnodynastes lanei 0 0 0 0 1 0 0 0 0 0 0 0 Thamnodynastes rutilus 0 0 0 0 0 0 0 0 0 0 0 0 Thamnodynastes strigatus 0 0 0 0 0 0 0 0 0 0 0 0 Trilepida brasiliensis 0 0 0 0 0 0 0 0 0 0 0 0 Trilepida koppesi 0 0 0 0 0 0 0 0 0 0 0 0 Typhlops brongersmianus 0 0 0 0 0 0 1 0 0 0 0 0 Typhlops reticulatus 0 0 0 0 0 0 0 0 0 0 0 0 Xenodon dorbignyi 0 0 0 0 0 0 0 0 0 0 0 0 Xenodon histricus 0 0 0 0 0 0 0 0 0 0 0 0 Xenodon matogrossensis 0 0 0 0 0 1 1 0 0 0 0 0 Xenodon merremii 1 1 0 1 0 1 1 1 0 0 0 1 Xenodon nattereri 0 0 0 0 0 0 0 0 0 0 0 0 Xenodon pulcher 0 1 0 1 0 0 0 0 0 0 0 0 Xenodon rhabdocephalus 0 0 0 0 0 0 0 0 0 0 0 0 Xenodon semicinctus 0 0 0 0 0 0 0 0 1 0 0 1 Xenodon severus 0 0 0 0 0 0 0 0 0 0 0 0 Xenopholis undulatus 0 0 0 0 0 0 0 0 0 0 0 0 Xenopholis werdingorum 0 0 0 0 0 0 0 0 0 0 0 0
69
49 50 51 52* 53 54 55 56 57 58 59 60* Phimophis vittatus 0 0 0 0 0 0 0 0 0 0 0 0 Phrynonax poecilonotus 0 0 0 0 0 0 0 0 0 0 0 0 Pseudoboa coronata 0 0 0 0 0 0 0 0 0 0 0 0 Pseudoboa nigra 0 0 0 0 0 1 1 1 0 0 0 0 Pseudoeryx plicatilis 0 0 0 1 0 1 1 0 0 0 0 0 Psomophis genimaculatus 0 1 0 0 0 1 1 0 0 0 0 0 Psomophis obtusus 0 0 0 0 0 0 0 0 0 0 0 0 Rena unguirostris 0 0 0 0 0 0 0 0 0 0 0 0 Rhachidelus brazili 0 0 0 0 0 1 0 0 0 0 0 0 Sibynomorphus lavillai 0 1 0 0 0 0 0 0 0 0 0 0 Sibynomorphus mikanii 0 0 0 0 0 1 1 1 0 0 0 0 Sibynomorphus turgidus 0 1 0 1 0 1 1 0 0 0 0 0 Sibynomorphus ventrimaculatus 0 0 0 0 0 1 1 1 0 0 0 0 Simophis rhinostoma 0 0 0 0 0 0 0 1 0 0 0 0 Siphlophis compressus 0 0 0 0 0 0 0 0 0 0 0 0 Spilotes sulphureus 0 0 0 0 0 0 0 0 0 0 0 0 Spilotes pullatus 0 0 0 0 0 1 0 0 0 0 0 0 Tachymenis peruviana 0 0 0 0 0 0 0 0 1 0 0 0 Taeniophallus occipitalis 0 0 0 0 0 0 1 0 0 0 0 0 Tantilla melanocephala 0 0 0 0 0 1 1 0 0 0 0 0 Thamnodynastes chaquensis 0 0 1 1 1 1 1 0 0 0 0 1 Thamnodynastes hypoconia 0 0 0 1 0 1 1 0 0 0 0 0 Thamnodynastes lanei 0 0 0 1 0 1 0 0 0 0 0 0 Thamnodynastes rutilus 0 0 0 0 0 0 0 0 0 0 0 0 Thamnodynastes strigatus 0 0 0 0 0 0 0 0 0 0 0 0 Trilepida brasiliensis 0 0 0 0 0 0 0 0 0 0 0 0 Trilepida koppesi 0 0 0 0 0 0 0 1 0 0 0 0 Typhlops brongersmianus 0 0 0 0 0 1 1 1 1 0 0 0 Typhlops reticulatus 0 0 0 0 0 0 0 0 0 0 0 0 Xenodon dorbignyi 0 0 0 0 0 0 0 0 0 0 0 0 Xenodon histricus 0 0 0 0 0 0 0 0 0 0 0 0 Xenodon matogrossensis 0 0 0 0 0 1 1 0 0 0 0 0 Xenodon merremii 0 0 1 0 1 1 1 0 0 0 0 0 Xenodon nattereri 0 0 0 0 0 0 1 0 0 0 0 0 Xenodon pulcher 0 1 1 1 0 0 0 0 0 0 1 0 Xenodon rhabdocephalus 0 0 0 0 0 0 0 0 0 0 0 0 Xenodon semicinctus 0 0 0 0 0 0 0 0 0 0 0 0 Xenodon severus 0 0 0 0 0 0 0 0 0 0 0 0 Xenopholis undulatus 0 0 0 0 0 0 0 0 0 0 0 0 Xenopholis werdingorum 0 0 0 0 0 0 0 0 0 0 0 0
70
61* 62* 63* 64 65 66 67 68* 69* 70* 71 72 Phimophis vittatus 0 0 0 0 0 0 0 0 0 0 0 0 Phrynonax poecilonotus 0 0 0 0 0 0 0 0 0 0 0 0 Pseudoboa coronata 1 0 0 0 0 0 0 1 0 0 0 0 Pseudoboa nigra 1 1 1 0 0 0 0 1 1 1 0 1 Pseudoeryx plicatilis 1 1 1 0 0 0 0 1 0 0 0 0 Psomophis genimaculatus 1 1 1 0 0 0 0 1 1 0 0 0 Psomophis obtusus 0 0 0 0 0 0 0 0 0 0 0 0 Rena unguirostris 0 0 0 0 0 0 0 0 0 0 0 0 Rhachidelus brazili 0 0 0 0 0 0 0 0 0 0 0 0 Sibynomorphus lavillai 1 0 0 0 0 0 0 0 0 0 0 0 Sibynomorphus mikanii 1 1 1 1 0 0 0 1 1 0 1 0 Sibynomorphus turgidus 1 0 1 0 0 0 0 1 1 0 1 0 Sibynomorphus ventrimaculatus 1 0 1 0 0 0 0 0 0 0 0 0 Simophis rhinostoma 0 0 1 1 0 0 0 0 0 1 1 0 Siphlophis compressus 0 0 0 0 0 0 0 0 0 0 0 0 Spilotes pullatus 1 0 1 1 0 0 0 1 0 0 0 0 Spilotes sulphureus 0 0 0 0 0 0 0 0 0 0 0 0 Tachymenis peruviana 0 0 0 0 0 0 0 0 0 0 0 0 Taeniophallus occipitalis 1 1 1 0 0 0 0 1 1 0 0 1 Tantilla melanocephala 1 1 1 0 0 0 0 0 1 0 0 1 Thamnodynastes chaquensis 1 1 1 0 0 0 0 1 1 0 0 0 Thamnodynastes hypoconia 1 0 1 0 0 0 0 1 1 0 0 1 Thamnodynastes lanei 1 1 0 0 0 0 0 1 0 0 0 0 Thamnodynastes rutilus 0 0 0 0 0 0 0 0 0 0 0 0 Thamnodynastes strigatus 0 0 0 0 0 0 0 0 0 0 0 0 Trilepida brasiliensis 1 0 0 0 0 0 0 0 1 0 0 0 Trilepida koppesi 0 0 0 0 0 0 0 0 1 0 0 0 Typhlops brongersmianus 1 1 0 0 0 1 0 1 1 0 0 0 Typhlops reticulatus 0 0 0 0 0 0 0 0 0 0 0 0 Xenodon dorbignyi 0 0 0 0 0 0 0 0 0 0 0 0 Xenodon histricus 0 0 0 0 0 0 0 0 0 0 0 0 Xenodon matogrossensis 1 1 1 0 0 0 0 1 1 0 1 0 Xenodon merremii 1 1 1 1 1 0 0 1 1 1 1 0 Xenodon nattereri 0 0 0 0 0 0 0 0 0 0 1 0 Xenodon pulcher 0 0 0 0 0 0 0 0 0 0 0 0 Xenodon rhabdocephalus 0 0 0 0 0 0 0 0 0 0 0 0 Xenodon semicinctus 0 0 0 0 0 0 0 0 0 0 0 0 Xenodon severus 0 0 0 0 0 0 0 0 0 0 0 0 Xenopholis undulatus 0 0 0 0 0 0 0 0 0 0 0 0 Xenopholis werdingorum 1 0 0 0 0 0 0 0 0 0 0 0
71
73* 74* 75 76 77 78* 79* 80* 81 82 83 84 Phimophis vittatus 0 0 0 0 0 0 0 0 0 0 0 0 Phrynonax poecilonotus 0 0 0 0 0 0 0 0 0 0 1 0 Pseudoboa coronata 1 0 0 0 0 0 0 0 0 0 0 0 Pseudoboa nigra 1 1 1 1 0 1 1 1 0 1 1 1 Pseudoeryx plicatilis 1 0 0 0 0 1 1 0 0 0 1 1 Psomophis genimaculatus 0 0 0 0 1 1 1 0 0 0 0 1 Psomophis obtusus 0 0 0 0 0 0 0 0 0 0 0 0 Rena unguirostris 0 0 0 0 0 0 0 0 0 0 0 0 Rhachidelus brazili 0 0 1 0 0 0 0 0 0 0 0 1 Sibynomorphus lavillai 0 0 0 0 0 0 0 0 0 0 0 0 Sibynomorphus mikanii 0 0 1 0 0 0 1 0 1 0 1 1 Sibynomorphus turgidus 1 0 1 0 0 1 1 0 0 1 1 1 Sibynomorphus ventrimaculatus 0 0 0 0 0 0 0 0 0 0 0 0 Simophis rhinostoma 0 0 0 0 0 0 0 0 0 0 0 0 Siphlophis compressus 0 0 0 0 0 0 0 0 0 0 1 0 Spilotes pullatus 1 0 1 0 0 1 1 0 1 1 1 1 Spilotes sulphureus 0 0 0 0 0 0 0 0 0 0 1 1 Tachymenis peruviana 0 0 0 0 0 0 0 0 0 0 0 0 Taeniophallus occipitalis 1 0 1 1 0 0 1 0 0 0 1 1 Tantilla melanocephala 1 0 1 0 0 1 0 0 0 0 1 1 Thamnodynastes chaquensis 1 0 0 0 1 1 1 0 0 0 1 1 Thamnodynastes hypoconia 0 0 0 1 0 0 1 1 1 0 1 1 Thamnodynastes lanei 1 0 0 0 0 1 1 0 0 0 0 1 Thamnodynastes rutilus 0 0 1 0 0 0 0 0 0 0 0 0 Thamnodynastes strigatus 0 0 0 0 0 0 0 0 0 0 0 0 Trilepida brasiliensis 1 0 0 0 0 0 0 1 0 0 0 1 Trilepida koppesi 0 0 1 0 0 0 0 0 0 0 0 1 Typhlops brongersmianus 1 1 1 0 1 1 1 0 1 1 1 1 Typhlops reticulatus 0 0 0 0 0 0 0 0 0 0 0 0 Xenodon dorbignyi 0 0 0 0 0 0 0 0 0 0 0 0 Xenodon histricus 0 0 0 0 0 0 0 0 0 0 0 0 Xenodon matogrossensis 0 0 0 0 1 1 0 1 1 0 0 1 Xenodon merremii 1 0 1 0 1 1 1 1 0 0 0 1 Xenodon nattereri 0 0 1 0 0 0 0 0 0 1 0 1 Xenodon pulcher 0 0 0 0 0 0 0 0 0 0 0 0 Xenodon rhabdocephalus 0 0 0 0 0 0 1 0 0 1 0 0 Xenodon semicinctus 0 0 0 0 0 0 0 0 0 0 0 0 Xenodon severus 0 0 0 0 0 0 0 0 0 1 1 1 Xenopholis undulatus 0 0 0 0 0 0 0 0 0 0 0 0 Xenopholis werdingorum 1 0 0 0 0 1 1 1 0 0 0 1
72
85 86 87 88 89 Phimophis vittatus 0 0 0 0 0 Phrynonax poecilonotus 0 0 0 0 0 Pseudoboa coronata 0 0 0 0 0 Pseudoboa nigra 1 0 0 0 1 Pseudoeryx plicatilis 0 0 0 0 0 Psomophis genimaculatus 0 0 0 0 0 Psomophis obtusus 0 0 0 0 0 Rena unguirostris 0 0 0 0 0 Rhachidelus brazili 1 0 0 0 0 Sibynomorphus lavillai 0 0 0 0 0 Sibynomorphus mikanii 1 0 1 0 1 Sibynomorphus turgidus 1 0 0 1 0 Sibynomorphus ventrimaculatus 0 0 0 0 0 Simophis rhinostoma 0 0 0 0 0 Siphlophis compressus 0 0 0 0 0 Spilotes pullatus 1 0 0 0 1 Spilotes sulphureus 0 0 1 0 0 Tachymenis peruviana 0 0 0 0 0 Taeniophallus occipitalis 1 0 0 0 1 Tantilla melanocephala 1 0 0 1 1 Thamnodynastes chaquensis 0 0 0 0 0 Thamnodynastes hypoconia 1 1 0 0 1 Thamnodynastes lanei 0 0 0 0 0 Thamnodynastes rutilus 0 0 0 0 0 Thamnodynastes strigatus 0 0 0 0 0 Trilepida brasiliensis 0 0 0 0 1 Trilepida koppesi 0 0 0 0 0 Typhlops brongersmianus 1 0 0 0 1 Typhlops reticulatus 0 0 1 0 0 Xenodon dorbignyi 0 0 0 0 0 Xenodon histricus 0 0 0 0 0 Xenodon matogrossensis 0 0 0 0 0 Xenodon merremii 1 0 1 1 1 Xenodon nattereri 0 0 0 0 0 Xenodon pulcher 0 0 0 0 0 Xenodon rhabdocephalus 0 0 1 0 0 Xenodon semicinctus 0 0 0 0 0 Xenodon severus 0 0 1 0 1 Xenopholis undulatus 0 0 0 0 0 Xenopholis werdingorum 1 0 0 0 1
73
CHAPTER 2
Relative Importance of Flooding as Driver of Snakes Species Turnover
in Wetlands in Central South America
Liliana Piatti1*, Dan F. Rosauer2, Cristiano de C. Nogueira 3, Vanda Lúcia Ferreira4 and Marcio
Martins 3
¹Programa de Pós-Graduação em Ecologia, Instituto de Biociências, Universidade de São
Paulo, São Paulo, São Paulo, Brazil
2College of Medicine, Biology and Environment, Australian National University, Canberra,
Australian Capital Territory, Australia
3 Departamento de Ecologia, Instituto de Biociências, Universidade de São Paulo, São Paulo,
São Paulo, Brazil
4 Centro de Ciências Biológicas e da Saúde, Universidade Federal de Mato Grosso do Sul,
Campo Grande, Mato Grosso do Sul, Brazil
*Corresponding author
Email: [email protected] (LP)
Short title: Flooding as driver of snake species turnover in central South America
74
1 ABSTRACT
2 In floodplains, cycles of flooding are considered key factors that activate ecological
3 processes and control both the spatial and temporal distribution of organisms as well their
4 life-history strategies. Several seasonally flooded areas occur in the Paraguay River Basin
5 (PRB), including the Pantanal, the largest continuous tropical floodplain. The Pantanal biota
6 has lower richness than surroundings areas and the hardships imposed by flooding events
7 have been cogitated as one of the causes of this pattern. Herein our objective was to
8 investigate the relative importance of flooding as a driver of snake beta diversity in the PRB
9 compared to the importance of other environmental, climatic, and historical factors that
10 could affect the beta diversity in the region. We used generalized dissimilarity models to
11 model the beta diversity of snake communities and its components (species turnover and
12 richness) as a function of environmental and spatial variables. Among seven predictors
13 considered, forest cover, geographical distance, flooding, and minimum temperature
14 contributed more to explain beta diversity. Contrarily to our expectation, forest cover was
15 the most important predictor, acting mainly on the communities’ species richness. A balance
16 between availability of suitable habitats for arboreal species and heterogeneity of
17 thermoregulatory conditions might be acting to shape the distributions of snakes through
18 gradient of forest cover in the PRB. The turnover of species between communities was
19 better explained by minimum temperature of the coldest month. Differences among species
20 in their ability to deal with extreme events may be mediating the species turnover between
21 areas. The action of flood on beta diversity was mainly on the species turnover. The
22 alternation of floods and droughts, coupled with the high variation in the inter-annual flood
23 cycles in the Pantanal area, may be favoring the occurrence of more plastic species in
24 seasonal flooded areas and restricting the occurrence of species which are more adapted to
25 a particular life strategy.
75
26 INTRODUCTION
27 Species composition in biological communities is a result of interactions of the
28 evolutionary history of both organisms and environments with local factors that currently
29 mediate species occurrence and coexistence, in addition to past and present stochastic
30 events [1]. Since the range of species and the turnover in community composition can be
31 seen as result of process occurring at different temporal and spatial scales [2, 3], the
32 understanding of the processes that drive species composition patterns depends on an
33 exploration of multiple factors [4, 5, 6]. Much of the communities idiosyncrasy derives from
34 the differences in prevalence and strength of a set of process acting on them [3].
35 In floodplains the flood pulses are considered a key ecological process [7, 8, 9]. They
36 drive important seasonal ecosystem changes [8, 10] and trigger ecological processes that
37 control both the spatial and temporal distribution of organisms as well their life-history
38 strategies [11, 12, 13]. Species adaptations to river flow regimes range from change in
39 individuals’ behavior to change in specie morphology or life cycles [14, 15, 16].
40 The largest continuous tropical floodplain is the Pantanal [8], located at the centre
41 of South America, in the depression of the Paraguay River Basin (PRB). This basin
42 encompasses other seasonal flooded areas and eight terrestrial ecoregions [17], being one
43 of the most ecologically and geologically heterogeneous areas of South America. As a
44 consequence of the annual flood pulse, a gradient in flood level results in a range of major
45 habitats forming a complex environmental mosaic [18]. The complex vegetative cover and
46 high seasonal productivity support an abundant fauna, composed by species representatives
47 of different ecoregions that surrounding the floodplain [19, 20]. Nevertheless, Pantanal
48 biota shows lower richness than the biota of surrounding regions [19], and that has been
49 attributed to its recent formation – 2,5 Ma, during the last Andean orogeny phase [21] – and
50 the ecological hardships imposed on the organism by the flood pulse [19, 20, 22].
76
51 Snakes are distributed across the globe, in a wide variety of environments [23]. This
52 broad range has been attributed to their great adaptability to available resources and high
53 speciation rates [23, 24]. Patterns of community richness and composition are highly
54 diversified. In South America, historical factors were suggested to be more important than
55 current environment in regional scales [25, 26, 27]. However, global patterns of reptile
56 distributions indicate that the current physical environment constrains the spatial
57 distribution of species [28] and recent studies found that the low levels of plasticity in
58 habitat use of some species restrict their range in some areas of Neotropical region [29, 30].
59 Snake life histories are affected by environmental factors such as temperature, rainfall and
60 environmental seasonality [31, 32, 33, 34, 35].
61 Most research about the effects of floods on biological communities was conducted
62 using plants and invertebrates as study subjects [e.g. 9, 11, 14, 15, 16]. They point that
63 adverse effects from flooding are responsible for changes on distribution and species
64 composition for several taxa [9, 11, 14, 15, 16, 20] and that different flow regime
65 parameters (such as flooding frequency, duration and predictability) can affect how
66 organisms adapt or fail to adapt to flooding [14].
67 In the current study we analyze the relative importance of flood as driver of snake
68 beta-diversity in the Paraguay River Basin. We expect that, given the high heterogeneity of
69 this region, an array of climatic, physical and historical factors influence the beta-diversity
70 between communities. Moreover, considering the large area that is periodically affected by
71 flooding and the magnitude of change that this event promotes on ecosystems, we believe
72 flooding is a key factor that can directly mediate the occurrence of different snakes in this
73 basin, either by shaping the replacement or the loss of species.
74
75 MATERIALS AND METHODS
77
76 As a first step to understand community composition we modeled the beta diversity
77 of PRB snakes communities as a function of biogeographical and environmental
78 dissimilarities within the area where they are placed, and of the geographical distance
79 between them. We then investigated how each predictor can explain the total beta
80 diversity, the species turnover and the difference in richness detected between any two
81 areas, considering taxonomic and phylogenetic diversity.
82 Study area
83 The Paraguay River basin (PRB) is located between 14° and 27° S and 53° and 67° W
84 (Fig 1). The entire catchment area covers 1,135,000 km2, and includes almost all of Paraguay
85 and parts of Bolivia, Brazil and Argentina. The basin includes the Pantanal and eight other
86 ecoregions [17] and four biogeographical subregions [36]. The limits of the Paraguay basin
87 and the Pantanal adopted here follow Petry and Sotomayor 2009 [37] and Hamilton et al.
88 1996 [38], respectively.
89 The Pantanal is situated in the upper Paraguay River depression. The area of about
90 140,000 km2 is subject to an annual, predictable, monomodal flood pulse [21]. During the
91 rainy season (November–March) the vast plain stores the water flowing from uplands and
92 delivers it slowly to the lower sections of the Paraguay River during the dry season (April–
93 October). On average, about one-third of the Pantanal area is inundated each year, with
94 monthly estimates of total flooded area ranging from 10% to 70% of the entire Pantanal
95 depression [38]. Because of the very slight slope of the terrain (2 to 3 cm per km from North
96 to South and 5 to 25 cm from East to West) floodwaters require about four months to pass
97 through the entire Pantanal [38].
78
98
99 Figure 1. Map showing the limits of Paraguay River Basin (outer black line) and its flooded 100 areas, the biogeographic provinces that it encompasses and the Pantanal (internal black 101 line). The 31 dotted boxes were the 0.5x0.5 degree cells used to delimit snakes 102 communities.
103 Biological Data
104 The species turnover between different snake communities of Paraguay River Basin
105 (PRB) was based on a database with records of snake occurrence in the region, gathered
106 from scientific collections and literature (for details see chapter 1 [39]). To delimit our
107 sample units (communities), we superimposed this species occurrence dataset on a 0.5 x 0.5
108 degree grid that covered the entire basin. We then considered the species recorded within
109 each grid cell to comprise a separate snake community. From 510 grid cells that covered the
79
110 PRB area, 31 were used in our analysis (Fig 1). These cells were chosen in order to include
111 communities distributed throughout the PRB and to only use relatively well-sampled
112 communities to analyze species turnover. That is, we compared the communities of these
113 grid cells with studies from the same ecoregions [40, 41, 42, 43] and similar latitudes, and
114 only included a cell in our analysis if the species richness was similar to that found in those
115 studies. Snake taxonomy followed Zaher et al. (2009) [44], Grazziotin et al. (2012) [45] and
116 Jadin et al. (2014) [46]. Species of the Anomalepidae and Aniliidae family were not
117 considered in our analyses. Species from Anomalepididae that occurs in the PRB are
118 taxonomically poorly resolved [47] and, such as Aniliidae have a much smaller probability of
119 detection compared to other species, especially when no systematized method is used. In
120 addition, these taxa are the most ancient of the entire basin, with much older relationships
121 than those between species from other snake families. Considering these facts in
122 conjunction, including species from these clades could cause undesired bias by inflating the
123 phylogenetic variability of the communities where they are present, masking the patterns
124 exhibited by other species.
125 Environmental and Historical Turnover Predictors
126 Considering the importance of selecting environmental variables that are relevant
127 when analyzing species occurrence [48, 49], we sought predictors to represent physiological
128 and physical limitations of species’ ability to use an area, in addition to predictors related to
129 the biogeographical history of each area. Furthermore, to account for physical barriers and
130 spatial autocorrelation we considered geographic distances between communities (grid
131 cells), because simultaneously accounting for the effects of spatial distance and
132 environment can result in better estimates of the contribution of environmental variables to
133 the resulting compositional divergence [50].
134 We used three climatic variables, here considered as surrogates for the physiological
135 limitations of snake ranges: isothermality, minimum temperature of the coldest month and
80
136 precipitation of the driest quarter of the year. These were obtained from the WorldClim
137 database (www.worldclim.org [51]) and were chosen for their importance for snake
138 physiology [52, 53]. Also, in PRB, these variables showed lower correlations (r2 <0.35)
139 between them than the set available in the WorldClim database. For each grid cell we
140 calculated the average value of each variable from layers with 10 minutes (around 18 km) of
141 resolution. Temperature is expressed in oC *10.
142 Variables describing intensity of flood and presence of forest in the area were used
143 as surrogates for physical limitations on microhabitat use, as microhabitat specialization has
144 been previously reported for snakes [54, 55] and is often expected for tropical species [56].
145 The percent of forest cover in a grid cell was calculated based on the sum of the presence of
146 two land cover classes (Evergreen and Deciduous Broadleaf Forests) obtained from EarthEnv
147 database (www.earthenv.org/landcover [57]). These two classes include virtually all kinds of
148 vegetation that can form a forest cover, with an arboreal substrate, occurring in PRB area
149 [58] (see Tuanmu and Jetz 2014 [57] for details from each class). Because we are considering
150 variables that could have acted on species occurrence along their evolutionary history, we
151 corrected the values of Deciduous Broadleaf presence for cells between -21.8 and -23.8 S
152 and -59.2 and -60.7 W. In those cases the current values of presence of this vegetation result
153 from of anthropic deforestation in recent decades [59], so we used the average value found
154 in neighboring cells.
155 Two flood variables were created by calculating the grid cell percent cover that is
156 characterized as floodplains, based on a map of seasonally flooded areas [60] obtained from
157 www.worldwildlife.org/pages/global-lakes-and-wetlands-database. These variables measure
158 the total area flooded within a grid cell and also in its surrounding areas (within a 0.5 degree
159 buffer).
160 We used a biogeographical distance variable as a surrogate of historical information
161 on the communities. Biogeographical distance was derived from the hierarchical
81
162 biogeographical regionalization of Morrone 2014 [36]. Following this regionalization, PRB
163 covers four biogeographical subregions and six provinces: South Brazilian Dominion, with
164 Rondonia and Yungas provinces; Chacoan Dominion, with Cerrado and Chacoan provinces;
165 Paraná Dominion, with Paraná Forest province; and South American Transition Zone, with
166 the Puna province [36]. Pairs of grid cells from the same biogeographical province were
167 given a biogeographical distance of zero; pairs from the same subregion but different
168 provinces, 1; and pairs from the different subregion, a value of 2.
169 Diversity Modelling
170 We investigated the relative importance of each predictor as driver of beta-diversity
171 using Generalised Dissimilarity Modelling (GDM) [61], a multivariate extension of Mantel
172 correlation analysis [62]. GDM models dissimilarity in assemblage composition between
173 pairs of locations as a nonlinear function of environmental differences between the
174 locations and their geographical distance [61]. Unlike other diversity modeling approaches,
175 GDM can accommodate the curvilinear relationship between environmental or geographic
176 separation between sites and compositional dissimilarity. Also, it considers the variation in
177 the rate of dissimilarity at different positions along environmental gradients [61]. This
178 capacity comes from the use of a link function to access the non-linearity of the relationship
179 between compositional and environmental distance, added to the use of I-splines functions
180 for each environmental attribute [61].
181 Our species presence-absence matrix was used to derive the Jaccard dissimilarity
182 measure of inter-site dissimilarity as the biological response variable for the GDM. We
183 started the modelling using the seven environmental predictors described above
184 (isothermality, minimum temperature of the coldest month, precipitation of the driest
185 quarter of the year, flood percentage in a grid cell, flood percentage in grid cell
186 surroundings, percentage of trees in a grid cell and biogeographical distance) and the
82
187 geographic distance, and we further reduced this set using 2 steps of backward-elimination
188 variable selection.
189 In the first step of elimination, variables with all coefficients of I-splines = 0 were
190 dropped, as they have no relationship with the biological pattern [61]. In the second step,
191 we successively removed predictors contributing less than 0.1% to the explained deviance
192 [61, 63]. The significance of the final model was calculated by permutations of the site-pair
193 table 999 times by randomizing the order of the rows [50, 64]. New sets of GDM’s were
194 fitted to these permutated site-pair tables to estimate an overall p-value for model
195 significance. We used the “gdm” package [65] for R software [66] to implement the
196 modeling and significance value. Because the attribute biogeographical distance is not a
197 continuous variable, we set the knots of the corresponding I-splines to 1,2 and 3, which are
198 the values that can be reached by this attribute in our case. For all other variables, we used
199 knots set at 0 (minimum), 50 (median), and 100 (maximum) percentiles [61].
200 We then repeated the modeling process using the phylogenetic dissimilarity
201 between sites as a response variable. Compared to species turnover, patterns of
202 phylogenetic turnover can provide extra information about the spatial structure of
203 biodiversity, for example providing more informative comparisons between the biota of
204 sites which share no species [63]. Phylogenetic dissimilarity was calculated for each grid cell
205 pair using PhyloSor Index [67], which computes the proportion of branch length of shared
206 species relative to total branch length of a phylogenetic tree of all species in two
207 communities. We used Mesquite 2.75 to assemble by hand a composite phylogeny of the
208 snakes of Paraguay River basin, based primarily on Tonini et al. 2016 [68] and Pyron et al.
209 2013 [69] and then collating information from various additional phylogenies (see details in
210 chapter 1 [39]). The placement of species that were not included in the published
211 phylogenies was inferred according to the relationships of sister species or included as a
212 polytomy in nodes containing its closely related species. Tree branch lengths were calibrated
83
213 using the BLADJ module of Phylocom 4.1 [70], using clade age estimates provided by Tonini
214 et al. 2016 [68]. Undated nodes were evenly interpolated between dated nodes.
215 Considering that the use of deep branches of the tree may blur rather than
216 strengthen the relationship between phylogenetic and environmental dissimilarity [63], we
217 fitted models for different ages within the phylogeny to find the phylogenetic tree depth at
218 which the relationship to current day environment is greatest. We generated 9 versions of
219 our species phylogeny, removing in each version any branches, or part branches, older than
220 a specified cutoff age, and created a root polytomy at that point. All portions of the tree
221 closer to the tips than this cutoff were retained unchanged (see Rosauer et al. 2014 [63] to
222 more details about the method). The cutoff age started including 90% of the tree depth and
223 eliminated other 10% of total depth in each phylogeny version. For each of the modified
224 tree, we then calculated the PhyloSor metric between site pairs and fitted the GDM model
225 in the same way described above.
226 After the entire modeling process was finalized, we used the model with the largest
227 amount of explained deviance to understand the origins of beta diversity. We partitioned
228 the beta diversity calculated from our presence-absence matrix in two additive fractions,
229 dissimilarity due to species replacement and dissimilarity due to richness differences,
230 following Carvalho et al. 2012 [71] and Cardoso et al. 2014 [72]. Considering that beta
231 diversity patterns can originate from two manners the analysis of the relative importance of
232 predictors on different fractions of beta diversity independently should reflect the
233 importance of each variable in these different process. The values of Jaccard beta diversity
234 and it components were calculated by package BAT [73].
235 RESULTS
236 A total of 122 snake species occurred in the 31 grid cells (Appendix S1). The GDM
237 model for taxonomic diversity accounted for 42% (p < 0,001) of the deviance in observed
84
238 turnover of PRB snake species (Fig 2). The variables isothermality, precipitation of the driest
239 quarter of the year and biogeographical distance showed no relationship with the diversity
240 gradient and were eliminated during the process of model selection.
241 The variables that most contributed to increase the explained deviance of the model
242 were forest cover followed by geographical distance. Cover of flooded area in the grid cell,
243 flood in surroundings areas and minimum temperature of the coldest month were
244 important as well, as the related variables helped to increase the explained model deviance
245 (Table 1). But, in those cases, the contribution to the dissimilarity between communities
246 were always less than one third of those shown by forest cover and geographical distance.
247 The total amount of beta diversity associated to each variable and the rate of variation of
248 beta diversity (and its variation) can be visualized in the maximum height and the slop of the
249 curves in Fig 2.
250 Table 1. Percentage of contribution to explained deviance of the model for each predictor.
Predictor edT edP edRep edRich
Forest cover 31 34 1 86
Geographical distance 19 16 16 1
Flooded area coverage 9 25 9 -
Flood coverage on 2 0.7 2 - surroundings area
Minimum temperature 1 - 22 -
Precipitation - 0.8 - -
251 edT: percentage of explained deviance considering taxonomic diversity; edP: considering 252 phylogenetic diversity; edRep: considering only the fraction due to species replacement; 253 edRich: percentage of explained deviance considering only the fraction due to species 254 richness divergence.
85
A B
C D
E F
255 Figure 2. Generalized dissimilarity model for snake composition in the Paraguay River 256 Basin using five predictor variables. A) Observed dissimilarity between pairs of 257 communities plotted against the ecological distance between areas where the 258 communities are placed. B-F) Fitted functions for each of the predictors.
86
259 The dissimilarity between communities increased in a nearly constant rate along the
260 gradients of geographical distance (expressed in degrees - Fig 2). Considering forest cover
261 and amount of flooded are in a grid cell gradients, the beta diversity increased substantially
262 only from 20% of cover. Considering the percent of flooded area at the surroundings of a
263 cell, the change in composition occurs in the beginning of the gradient and reaches a plateau
264 at the point from that the regions have 40% or more flooded areas in their surroundings.
265 The same occurs with minimum temperature, which show compositional differences from
266 12 to 15 degrees.
267 When phylogenetic distance between pairs of communities was considered as
268 response variable, the model explained 26% of total deviance (p=0.002). The predictor that
269 most contributed to the explained deviance of the model was forest cover (34%), followed
270 by flooded area in the grid cell (25%) and geographic distance (16%). Precipitation and flood
271 in surrounding areas was also considered a significant predictor in the phylogenetic distance
272 model, whereas isothermality, minimum temperature and biogeographical distance were
273 excluded of the model after the backward elimination process (Table. 1). The models fitted
274 to phylogenetic distances calculated with different phylogeny cutoff ages showed successive
275 decreases of the explained deviance, as larger amounts of ancient relationships were
276 considered - the model considering just the 10% most recent relationships between species
277 explained 39% of the deviance of the model, whereas the model considering 90% of
278 phylogenetic relationships explained 27% of the total model deviance (bars in Fig 3). This
279 result indicates that patterns of phylogenetic beta diversity do not provide, in this case,
280 extra information about the current spatial structure of biodiversity, as they are less
281 explained by predictors when compared to the model considering current species
282 composition. Cover of flooded area in a grid cell was the predictor most variable considering
283 the contribution to the explained deviance of the models fitted using different amount of
284 phylogenetic relationships. When the values of phylogenetic diversity were calculated
87
285 considering all phylogenetic relationships between species the importance of flood was
286 twice the value found when only the earlier relationships were considered (Fig 3.)
287 288 Figure 3. Variation of the percentage contribution to explained deviance of the model for 289 each predictor, related to the percentage of the phylogenetic tree considered to calculate 290 phylogenetic dissimilarity between communities. Bar height indicate the explained 291 deviance of the GDM fitted to each tree. Lines show the variation of the importance of 292 each predictor: asterisk represent forest cover, squares represent geographical distance, 293 diamonds represent flooded area cover, crosses represent flooded area cover in 294 surrounding areas, triangles are minimum temperature and circles represent precipitation.
295 As the model fitted considering species composition showed better fit than the
296 models constructed with phylogenetic turnover, only the species model was used to
297 understand the origins of beta diversity. The partition of beta diversity into two additive
298 components showed that the predictors have different relative importance to explain each
299 component. The model with dissimilarity due to species replacement as response variable
300 explained 9% of the total model deviance (p=0.004) and pointed to minimum temperature,
88
301 geographical distance and cover of flooded area as variables that best explained the
302 deviance (Table 1). And the model dissimilarity due to richness differences as the response
303 variable explained 7% of the total model deviance (p=0.003) and was explained only by
304 differences in forest cover and geographical distance (Table 1).
305 DISCUSSION
306 Contrary to our expectations, seasonal flooding was not the most important
307 predictor of snake beta diversity in the Paraguay River basin (PRB). Nevertheless, flooding
308 was a driver of composition of communities, acting mainly on the process of species
309 replacement, and showed increased importance when the phylogenetic beta diversity was
310 considered.
311 The most important variable to explain the beta diversity between snake
312 communities in the PRB was the percentage forest cover in a grid cell. It acts mainly on the
313 species richness, with communities from different parts of the gradient of forest cover being
314 a subset of all species present in the region. This result indicates that habitat use can be a
315 strong constraint on snake species ranges in our study region. Cavalheri et al. 2015 [30]
316 found that less vertically structured habitats represent a strong filter in Neotropical snake
317 communities, because arboreal and semi-arboreal species are less prone to occupy less
318 vertically structured vegetation types due to the lower abundance of adequate substrate.
319 This process can be in action in the PRB as well. For example, Lygophis flavifrenatus uses the
320 ground as well as bushes and trees [74] and in the PRB it was restricted to areas with at least
321 25% of tree cover. As another example, although the ecology of Apostolepis nigroterminata
322 is virtually unknown, it occurs in the south of the Amazonian Dominion and contact areas
323 with the Cerrado Dominion [75], suggesting a dependence on forested formations due their
324 edaphic features [75]. In the PRB A. nigroterminata was found only in grid cells with more
325 than 30% of tree cover.
89
326 However, some snakes from the PRB are able to occur throughout the available
327 gradient of forest coverage, and the stronger constraint on species occurrence seems exist
328 in areas with higher forest cover. About one quarter of the species are absent from areas
329 with more than 50% of tree coverage. Since suitability of thermoregulatory microhabitats is
330 very important for reptiles [76, 77, 78, 79] the absence of open areas with direct sun for
331 thermoregulation may be restricting the species distribution. Our results are in agreement
332 with other studies that found poorer communities of reptiles and amphibians in closed
333 forest areas than those of open areas of the Pantanal [80]. It also agrees with results which
334 showed higher reptile abundance in temperate savannas of North America than forested
335 areas in surrounding regions [81], and studies indicating that the thermal quality of dense
336 forests was inferior to that of open areas to different reptiles species, because basking in
337 open areas, well exposed to the sun, is an essential thermoregulatory activity of reptiles [82,
338 83]. A balance between availability of vertically structured habitats for arboreal species and
339 heterogeneity of thermoregulatory conditions might be acting to shape the range of snake
340 species through the PRB tree cover gradient.
341 In our models the minimum temperature of the coldest month was also an
342 important variable explaining the beta diversity, but acting mainly in the component
343 originated from the turnover of species, and not shaping the communities’ richness. Low
344 temperature is known to be a limiting factor for the activity of most snakes [84, 85] because
345 it can reduce their metabolic rates to extreme levels [78, 86]. So, the species difference in
346 the ability to deal with extreme events (such as minimum temperature) may be mediating
347 the species turnover between areas, while the relationship between temperature at a larger
348 temporal scale (such as monthly average temperature) and availability habitats to
349 thermoregulation might be restricting some species distributions in the PRB.
350 The high importance of geographical distance to explain beta diversity may be a
351 result of two non-mutually exclusive factors. As already found in other snake communities
90
352 [25, 30], neutral processes such as dispersal limitation seem also acting on turnover of PRB
353 snake species, and thus communities close to each other tend to be more similar than those
354 farther apart. Furthermore, there might be spatial autocorrelation among other predictors
355 that vary together and were not used in the models (e.g. average temperatures). Therefore,
356 the cause of similarities among communities close to each other might have been
357 mistakenly attributed only to their proximity, even though it could also be caused by their
358 similar climatic conditions that could respond to the non explained deviance of our model.
359 Considering the relatively small spatial scale of our work, the minor importance of
360 biogeographical distance to our beta-diversity model was expected. South American snake
361 communities from different latitudes tend to present different proportions of the three
362 main South American snake lineages (Colubridae, Dipsadinae, and Xenodontinae) reflecting
363 differences in centre of origin, ancestral ecological niche, dispersal limitation and time for
364 speciation [2, 25, 30] However, the communities we evaluated are less than 11 latitude
365 degrees apart from each other. At this spatial scale, historical patterns may be being
366 overcome by processes caused by currents conditions. Also within the PRB, large rivers can
367 act as dispersion corridors [39, 87] to some snake species, contributing to mix the fauna of
368 different origins [88].
369 While the model using species beta diversity as a response variable represents
370 currently occurring biota, whose distributions are mediated mostly by present
371 environmental factors, the model using phylogenetic beta diversity includes data from past
372 lineages (shared ancestry). Probably, the reduced model fits using phylogenetic compared to
373 taxonomic diversity occurred because the former can include features from past
374 environments or linages, that maybe are not conserved within extant taxa and
375 environments. Even so, the models using phylogenetic beta diversity agreed with the model
376 using taxonomic diversity in indicating forest cover and flooded area as the most important
377 predictors of the phylogenetic turnover between communities. Maybe the processes
91
378 shaping communities that can be in course nowadays at gradients of flooding and tree cover
379 can be on the mediation of the phylogenetic niche conservatism. That is, the communities
380 tend to be formed by related species, because phylogenetically related species are expected
381 to have similar phenotypic traits and life history strategies to establish in available
382 environments. In that case, the increased contribution of flood to explain the model
383 deviance as older phylogenetic relations are included in the calculation, may be an evidence
384 that the ability to occur or not in a flood are can be related to a ancient characters, e. g. had
385 generalist strategies and be able to deal with environments constantly perturbed [89].
386 Flood disturbance was reported as an important driver not only for traditional
387 species diversity measures, but also for the functional diversity of floodplain trees and
388 invertebrates in different regions [9, 14, 15], and the current species composition of a snake
389 community differed significantly between riparian and non-riparian areas in a Neotropical
390 rainforest [29]. In the PRB, the main action of flood on beta diversity was by the component
391 of species turnover. Maybe the alternation of floods and droughts, coupled with the high
392 variation in the inter-annual floods cycles at Pantanal area may favor the occurrence of
393 more plastic species in the floodplain, and restricting the occurrence of species more
394 adapted to a particular habitat or life strategy. The fact that the amount of flooded areas
395 surrounding a grid cell does not contribute as much as flooded area in the grid cell did in our
396 models, could suggest that the action of floods occurs in a direct and localized way. Seasonal
397 floods probably narrow availability of suitable conditions on a smaller scale, instead of acting
398 indirectly (for example, interfering in population establishment by the lack of migrants that
399 could reach a flooded area). However, some studies show that rivers and their flow regimes
400 can also act as a dispersal corridor for some species (e.g. species of the genus Helicops) [39,
401 87] and many of snakes of the PRB are present throughout all the gradient of flood and are
402 not negatively affected by seasonal flood regimes. Future studies of life strategies and
92
403 habitat use of the species able to use seasonally flooded habitats could elucidate which and
404 how process triggered by floods shape the species distributions, and how this occurs.
405 Some particular occurrence patterns highlight the interaction of multiple factors on
406 species turnover in the PRB. For example, there are three species of the Bothrops neuwiedi
407 complex in the region. This is a monophyletic, highly geographically structured species
408 group, widespread in South American open ecosystems [90]. These three species can be
409 found in sympatry, however B. diporus was present throughout the entire forest cover
410 gradient but was confined to areas with less flood (0 to 33% of flooded area). Bothrops
411 pauloensis occurs throughout the flood gradient but was present in cells with tree coverage
412 up to 30%. Finally, B. mattogrossensis occurred along all the variation of tree and flooded
413 area cover. These results indicates that B. mattogrossensis has a higher environmental
414 plasticity and can range through more areas of the PRB than the other two Botrhops species.
415 In fact, Martins et al. 2001 [91] suggested that although apparently terrestrial, B.
416 mattogrossensis has a relatively slender body and long tail when compared to other species
417 in the Bothrops neuwiedi complex, perhaps because this taxon could occasionally be forced
418 to climb the vegetation during seasonal floods.
419 Model fitting is usually based on pattern-recognition approaches, where
420 associations between geographic occurrence of a species and a set of predictor variables are
421 explored to allow or support statements of the mechanisms governing species’ distributions
422 [92]. Overall, our fitted models indicate that snake beta diversity in the PRB can be
423 understood as an aggregate property of a wide range of environmental conditions and biotic
424 interactions. Forest cover seems to be constraining the occurrence of some species in both
425 gradient extremities by absence of suitable conditions for habitat use or thermoregulation
426 behaviours. Floods interact with other environmental features and could be limiting the
427 range of some species that do not show adaptations for recurrent and seasonal flooding that
428 bring large alterations in the environments. Phenomena at different temporal and spatial
93
429 scales, like community level interactions and even spatial signal of evolutionary history also
430 act on community composition at some level. The search for evidences of environmental
431 filtering acting on the assemble of the different flooded area communities can help to show
432 how diversity patterns of these singular areas are created and maintained, and how the
433 organisms can adapt to new environmental conditions during their evolutionary history.
434 ACKNOWLEDGMENTS
435 We are grateful to Marcus Cianciaruso for useful comments and insights. V.L.F. thanks 436 FUNDECT (187/14), for partial financial support. C.N. thanks CNPq and FAPESP (2012/19858- 437 2) for postdoctoral fellowships. The authors declare no conflict of interest.
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698 SUPPORTING INFORMATION
699 S1 Table. Presence-absence of 122 snakes species in 31 0.5 x 0.5 degree grid cells in the 700 Paraguay River Basin. The first two lines indicate coordinates of the centroid of each cell.
102
701 SUPPORTING INFORMATION - Relative importance of flooding as drivers of snakes species turnover in wetlands in central South America
702 S1 Table. Presence-absence of 122 snakes species in 31 0.5 x 0.5 degree grid cells in the Paraguay River Basin. The first two lines indicate coordinates of the 703 centroid of each cell. 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 Long -56.0 -57.0 -56.0 -55.5 -57.5 -56.5 -57.0 -56.5 -55.0 -57.5 -59.5 -60.5 -57.5 -56.5 -59.5 -57.5 Lat -15.1 -15.6 -15.6 -15.6 -16.1 -16.1 -16.6 -16.6 -17.6 -18.1 -18.6 -19.1 -19.1 -19.1 -23.1 -19.6 Apostolepis ambiniger 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Apostolepis assimilis 0 0 1 1 0 0 0 1 1 0 0 0 0 0 0 0 Apostolepis dimidiata 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Apostolepis intermedia 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Apostolepis nigroterminata 0 0 0 0 0 0 0 1 0 1 0 0 1 0 0 0 Apostolepis vittata 1 0 1 1 1 0 0 0 0 0 0 0 0 0 0 0 Atractus albuquerquei 0 1 0 0 1 0 0 0 1 0 0 0 0 0 0 0 Atractus paraguayensis 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Boa constrictor 1 0 1 1 1 1 0 0 1 1 0 0 1 0 0 1 Boiruna maculata 1 0 0 0 0 1 0 0 0 0 0 0 1 1 0 0 Bothrops alternatus 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Bothrops diporus 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Bothrops mattogrossensis 1 0 1 0 1 1 1 1 0 0 1 1 1 1 0 1 Bothrops moojeni 1 1 1 1 1 1 0 0 1 1 0 0 1 0 0 1 Bothrops pauloensis 1 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 Chironius bicarinatus 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Chironius exoletus 1 1 1 1 1 0 0 0 0 1 0 0 1 0 0 0 Chironius flavolineatus 1 1 1 0 1 1 0 0 1 1 1 0 1 1 0 0 Chironius laurenti 0 1 1 0 0 1 1 1 0 1 0 0 1 0 0 1
103
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 Chironius quadricarinatus 1 0 1 1 1 1 1 0 0 0 0 0 1 0 1 1 Chironius scurrulus 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Clelia clelia 0 0 0 0 0 0 1 0 1 0 0 0 0 1 0 0 Clelia plumbea 1 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 Corallus hortulanus 1 1 1 1 0 0 0 0 0 0 0 0 1 0 0 0 Crotalus durissus 1 1 1 1 1 1 0 0 1 1 1 1 1 1 1 1 Dipsas indica 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 Drymarchon corais 1 1 1 0 1 1 0 1 0 1 1 0 1 0 1 0 Drymoluber brazili 1 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 Epicrates alvarezi 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Epicrates cenchria 0 0 1 0 0 0 0 0 1 0 0 0 0 0 0 0 Epicrates crassus 1 1 1 1 1 1 0 0 1 1 0 0 1 0 0 1 Erythrolamprus aesculapii 1 0 1 0 0 1 0 0 1 0 0 0 0 0 0 0 Erythrolamprus albertguentheri 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Erythrolamprus almadensis 1 1 1 1 1 1 0 0 0 1 0 0 0 1 0 0 Erythrolamprus frenatus 0 0 0 0 0 0 0 0 1 0 0 0 0 1 0 0 Erythrolamprus jaegeri 0 0 0 0 0 0 0 0 0 1 0 0 1 1 0 0 Erythrolamprus maryellenae 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Erythrolamprus miliaris 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Erythrolamprus poecilogyrus 1 0 1 1 1 1 1 1 1 1 0 0 1 1 1 1 Erythrolamprus reginae 1 1 1 1 1 1 1 1 1 1 1 0 1 0 0 0 Erythrolamprus sagittifer 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 Erythrolamprus semiaureus 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 Erythrolamprus taeniogaster 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 Erythrolamprus typhlus 1 0 0 0 0 1 0 0 0 1 0 1 1 1 0 0 Eunectes murinus 1 1 1 1 0 0 0 0 1 0 0 0 1 0 0 1
104
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 Eunectes notaeus 0 0 0 0 1 1 0 0 0 1 0 0 1 1 1 1 Helicops angulatus 1 0 1 1 1 0 0 0 1 0 0 0 0 0 0 1 Helicops infrataeniatus 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 Helicops leopardinus 0 1 1 0 1 1 1 1 0 1 0 0 1 1 1 1 Helicops modestus 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Helicops polylepis 1 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 Hydrodynastes bicinctus 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Hydrodynastes gigas 1 0 1 1 1 1 0 0 0 1 0 0 1 1 1 1 Hydrops caesurus 0 0 0 0 0 0 1 1 0 1 0 0 1 0 0 0 Imantodes cenchoa 1 0 1 1 0 1 0 0 0 0 0 0 1 0 0 0 Leptodeira annulata 1 1 1 1 1 0 0 0 0 1 1 0 1 1 1 1 Leptophis ahaetulla 1 1 1 1 1 1 1 1 1 1 0 0 1 1 1 1 Lygophis dilepis 0 0 1 0 0 1 0 0 0 0 0 0 0 0 1 1 Lygophis flavifrenatus 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Lygophis meridionalis 1 1 1 0 1 0 0 0 1 0 1 0 0 1 0 0 Lygophis paucidens 1 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 Mastigodryas bifossatus 1 1 1 1 1 1 0 0 1 0 0 0 1 1 1 1 Mastigodryas boddaerti 1 1 1 0 1 0 0 1 0 1 0 1 1 0 0 0 Micrurus baliocoryphus 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 Micrurus diana 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 Micrurus frontalis 1 1 1 1 0 0 0 0 1 0 0 0 0 1 0 0 Micrurus lemniscatus 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 Micrurus pyrrhocryptus 0 0 0 0 0 0 0 0 0 1 0 1 1 0 0 1 Micrurus surinamensis 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Micrurus tricolor 0 0 1 0 0 1 1 0 0 0 0 0 1 0 0 0 Mussurana bicolor 0 0 1 0 0 1 1 1 0 0 0 0 1 1 1 1
105
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 Oxybelis aeneus 1 0 1 1 0 0 0 0 0 1 0 0 1 0 0 0 Oxybelis fulgidus 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Oxyrhopus guibei 1 0 1 1 0 1 1 0 0 0 1 0 0 0 0 0 Oxyrhopus petolarius 1 0 1 0 0 1 0 0 0 1 0 0 1 0 0 1 Oxyrhopus rhombifer 1 1 1 1 1 1 0 0 0 1 0 1 1 1 1 1 Oxyrhopus trigeminus 1 1 1 1 1 1 0 0 1 0 0 0 1 1 0 0 Phalotris matogrossensis 0 1 1 0 1 1 1 0 0 1 0 0 1 1 0 0 Phalotris mertensi 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 Phalotris nasutus 0 0 0 0 0 0 0 0 1 0 0 0 0 1 0 0 Phalotris nigrilatus 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Phalotris tricolor 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0 0 Philodryas aestiva 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 Philodryas agassizii 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 Philodryas baroni 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 Philodryas livida 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 Philodryas mattogrossensis 0 0 0 0 1 0 1 0 1 0 0 0 1 0 1 0 Philodryas nattereri 1 1 1 1 0 1 0 0 1 1 0 0 0 0 0 0 Philodryas olfersii 1 1 1 1 0 1 0 1 1 1 1 1 1 1 0 1 Philodryas patagoniensis 1 0 1 1 0 1 0 0 1 1 0 0 1 0 0 1 Philodryas psammophidea 1 0 1 1 0 0 0 0 0 0 0 1 0 0 0 0 Philodryas viridissima 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 Phimophis guerini 1 0 1 1 0 0 0 0 0 0 0 0 1 0 0 0 Phimophis vittatus 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Phrynonax poecilonotus 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Pseudoboa coronata 0 0 0 0 0 0 0 0 0 1 0 0 1 0 0 0 Pseudoboa nigra 1 1 1 1 1 1 1 1 1 1 0 1 1 1 0 0
106
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 Pseudoeryx plicatilis 0 1 1 0 0 1 1 1 0 1 0 0 1 0 0 1 Psomophis genimaculatus 0 0 1 0 0 1 1 1 0 0 0 0 1 1 0 1 Rhachidelus brazili 0 0 1 0 0 0 0 0 1 0 0 0 0 0 0 0 Sibynomorphus lavillai 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0 1 Sibynomorphus mikanii 1 1 1 1 0 1 0 0 1 0 0 0 1 1 0 1 Sibynomorphus turgidus 0 1 1 1 1 1 0 0 1 1 1 1 1 1 0 1 Sibynomorphus ventrimaculatus 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Simophis rhinostoma 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Siphlophis compressus 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Spilotes pullatus 1 1 1 1 1 1 0 0 1 1 1 1 1 0 0 1 Spilotes sulphureus 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 Taeniophallus occipitalis 1 1 1 1 0 1 0 1 1 1 1 1 1 1 0 0 Tantilla melanocephala 1 1 1 1 0 0 1 0 1 1 0 0 1 1 0 0 Thamnodynastes chaquensis 0 1 1 0 0 1 1 0 0 1 0 0 1 1 0 1 Thamnodynastes hypoconia 1 1 1 0 0 1 0 0 0 0 0 0 1 1 0 0 Thamnodynastes lanei 0 0 0 0 1 1 0 1 0 1 0 0 1 0 0 1 Thamnodynastes rutilus 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 Xenodon matogrossensis 0 0 1 0 1 0 0 0 0 0 0 0 1 1 0 1 Xenodon merremii 1 0 1 1 1 1 0 0 1 1 1 1 1 0 1 1 Xenodon nattereri 0 0 1 0 0 0 0 0 1 0 0 0 0 0 0 0 Xenodon pulcher 0 0 0 0 0 0 0 0 0 0 1 1 0 0 1 0 Xenodon rhabdocephalus 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 Xenodon severus 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 Xenopholis undulatus 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Xenopholis werdingorum 1 0 1 1 1 1 0 1 0 1 0 0 1 0 0 0
107
17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 Long -57.0 -56.0 -60.0 -58.0 -57.0 -56.5 -56.0 -55.5 -55.0 -58.0 -56.5 -60.0 -56.0 -56.5 -57.5 Lat -19.6 -19.6 -20.1 -20.1 -20.1 -20.1 -20.6 -20.6 -20.6 -21.6 -21.6 -22.6 -22.6 -24.6 -25.1 Apostolepis ambiniger 0 0 0 0 0 1 0 0 0 0 0 0 0 0 1 Apostolepis assimilis 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Apostolepis dimidiata 0 1 0 0 0 1 1 1 0 0 1 0 1 1 1 Apostolepis intermedia 0 0 0 0 0 1 0 1 0 0 0 0 0 0 0 Apostolepis vittata 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Atractus albuquerquei 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Atractus paraguayensis 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 Boa constrictor 0 1 1 0 1 1 1 1 1 0 1 1 0 1 1 Boiruna maculata 0 0 1 0 0 1 0 0 0 0 0 1 0 0 1 Bothrops alternatus 0 1 0 0 0 0 1 0 1 0 1 0 0 1 1 Bothrops diporus 0 0 1 0 0 0 0 0 0 0 0 1 0 0 1 Bothrops mattogrossensis 1 1 1 1 1 1 1 0 0 1 1 1 1 0 0 Bothrops moojeni 1 1 0 1 0 1 1 1 1 1 0 0 1 0 0 Bothrops pauloensis 1 1 0 0 0 0 1 0 1 0 1 0 1 0 0 Chironius bicarinatus 0 1 0 0 0 0 1 0 0 0 0 0 0 0 0 Chironius exoletus 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 Chironius flavolineatus 0 1 0 0 0 1 1 1 0 0 1 0 1 0 0 Chironius laurenti 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Chironius quadricarinatus 1 1 1 0 0 1 0 1 0 0 1 1 1 0 1 Chironius scurrulus 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Clelia clelia 1 1 0 0 0 0 1 0 0 0 0 0 0 0 1 Clelia plumbea 1 1 0 0 0 1 0 0 0 0 0 0 0 0 1 Corallus hortulanus 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Crotalus durissus 1 1 1 0 1 1 1 1 1 1 1 0 1 1 1
108
17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 Dipsas indica 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Drymarchon corais 0 1 1 1 1 1 1 0 1 1 1 0 1 1 0 Drymoluber brazili 0 0 0 0 0 1 0 1 1 0 0 0 0 0 0 Epicrates alvarezi 0 0 1 0 0 0 0 0 0 0 0 1 0 0 0 Epicrates cenchria 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Epicrates crassus 0 1 0 0 1 1 0 0 1 0 1 0 0 1 0 Erythrolamprus aesculapii 0 1 0 0 0 1 1 0 0 0 1 0 1 1 1 Erythrolamprus albertguentheri 0 0 1 0 0 0 0 0 0 0 0 1 0 0 0 Erythrolamprus almadensis 0 1 0 0 0 1 1 1 0 0 1 0 0 1 0 Erythrolamprus frenatus 0 0 0 0 0 0 0 0 1 0 0 0 0 1 0 Erythrolamprus jaegeri 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 Erythrolamprus maryellenae 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Erythrolamprus miliaris 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Erythrolamprus poecilogyrus 1 1 1 1 1 1 1 1 0 1 1 1 1 1 1 Erythrolamprus reginae 1 0 0 0 1 1 0 0 0 0 0 0 0 1 0 Erythrolamprus sagittifer 0 0 1 0 0 0 0 0 0 0 0 1 0 0 0 Erythrolamprus semiaureus 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 Erythrolamprus taeniogaster 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Erythrolamprus typhlus 1 1 0 0 0 1 1 0 0 1 1 0 0 0 0 Eunectes murinus 1 1 0 1 0 1 1 1 0 0 1 0 1 0 0 Eunectes notaeus 1 0 0 1 1 1 1 0 0 0 0 0 0 1 1 Helicops angulatus 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Helicops infrataeniatus 0 0 0 0 0 0 0 0 1 0 0 0 1 0 0 Helicops leopardinus 1 1 0 1 0 1 1 1 0 1 0 0 0 1 1 Helicops modestus 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 Helicops polylepis 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
109
17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 Hydrodynastes bicinctus 0 0 0 0 0 1 1 0 0 0 0 0 0 0 0 Hydrodynastes gigas 1 1 0 1 0 1 1 0 0 1 0 0 0 0 1 Hydrops caesurus 0 1 0 0 0 1 0 0 0 0 1 0 0 0 0 Imantodes cenchoa 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Leptodeira annulata 1 1 1 0 1 1 1 0 0 1 1 1 0 0 1 Leptophis ahaetulla 1 1 0 1 1 1 1 1 0 1 1 0 1 1 1 Lygophis dilepis 1 0 0 1 1 1 0 0 0 1 1 1 0 0 1 Lygophis flavifrenatus 0 0 0 0 1 1 0 0 0 0 0 0 1 1 0 Lygophis meridionalis 0 1 0 0 0 1 1 1 1 0 1 0 1 1 1 Lygophis paucidens 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Mastigodryas bifossatus 0 1 0 0 1 1 1 1 1 0 1 1 0 1 1 Mastigodryas boddaerti 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Micrurus baliocoryphus 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 Micrurus diana 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Micrurus frontalis 0 0 0 0 1 1 1 0 1 0 1 0 1 1 1 Micrurus lemniscatus 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 Micrurus pyrrhocryptus 1 0 0 1 0 0 0 0 0 0 0 0 0 0 0 Micrurus surinamensis 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Micrurus tricolor 1 0 0 0 1 1 1 0 0 1 0 0 0 0 0 Mussurana bicolor 1 1 0 1 1 1 1 1 0 0 0 0 0 0 1 Oxybelis aeneus 0 1 0 0 1 1 1 0 0 0 0 0 0 0 0 Oxybelis fulgidus 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Oxyrhopus guibei 0 1 0 0 0 1 1 1 1 0 1 0 1 1 1 Oxyrhopus petolarius 0 1 0 1 1 1 1 0 0 0 0 0 0 0 0 Oxyrhopus rhombifer 1 1 1 1 0 1 1 0 0 1 0 1 0 0 0 Oxyrhopus trigeminus 0 1 0 0 1 1 1 1 0 0 0 0 0 0 0
110
17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 Phalotris matogrossensis 0 1 0 0 0 1 1 1 0 0 1 0 0 0 1 Phalotris mertensi 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Phalotris nasutus 0 1 0 0 0 0 0 1 0 0 0 0 0 0 0 Phalotris nigrilatus 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 Phalotris tricolor 0 0 0 0 0 0 0 0 0 0 0 1 0 0 1 Philodryas aestiva 0 0 0 0 0 1 1 0 1 0 0 0 0 1 0 Philodryas agassizii 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 Philodryas baroni 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 Philodryas livida 0 0 0 0 1 1 1 0 0 0 0 0 0 0 0 Philodryas mattogrossensis 1 1 1 0 1 1 1 1 1 1 1 1 0 0 0 Philodryas nattereri 0 0 0 0 1 1 1 1 1 0 0 0 0 0 0 Philodryas olfersii 0 1 0 1 1 1 1 1 1 0 1 1 1 1 1 Philodryas patagoniensis 1 1 0 1 0 0 1 1 0 1 0 0 1 1 1 Philodryas psammophidea 0 1 1 0 1 0 1 0 0 0 1 1 0 0 0 Philodryas viridissima 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Phimophis guerini 0 1 0 0 1 1 1 1 1 1 0 0 0 0 1 Phimophis vittatus 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 Phrynonax poecilonotus 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Pseudoboa coronata 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Pseudoboa nigra 1 1 0 0 1 1 1 1 1 1 1 0 1 0 0 Pseudoeryx plicatilis 1 1 0 1 0 1 1 0 0 0 0 0 0 0 0 Psomophis genimaculatus 1 1 1 0 0 1 1 0 0 1 0 1 0 0 0 Rhachidelus brazili 0 0 0 0 1 1 0 0 0 0 0 0 0 1 0 Sibynomorphus lavillai 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Sibynomorphus mikanii 1 1 0 0 1 0 0 0 1 1 1 0 0 0 0 Sibynomorphus turgidus 0 1 1 1 1 1 1 1 0 1 1 1 0 1 1
111
17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 Sibynomorphus v entrimaculatus 1 1 0 0 1 1 0 1 1 0 0 0 1 0 1 Simophis rhinostoma 0 1 0 0 0 0 0 0 1 0 0 0 0 0 0 Siphlophis compressus 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Spilotes pullatus 0 1 0 0 0 0 0 0 0 0 1 0 1 1 0 Spilotes sulphureus 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Taeniophallus occipitalis 0 1 0 0 0 0 1 1 0 1 1 0 1 0 1 Tantilla melanocephala 1 1 0 0 0 1 1 1 0 0 0 0 0 1 1 Thamnodynastes chaquensis 1 1 0 1 0 1 1 0 0 1 0 0 0 0 1 Thamnodynastes hypoconia 0 1 0 1 0 1 1 0 0 1 0 0 0 0 1 Thamnodynastes lanei 1 1 0 0 0 1 0 0 0 0 0 0 0 1 0 Thamnodynastes rutilus 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Xenodon matogrossensis 0 1 0 0 0 1 1 1 0 1 0 0 0 0 0 Xenodon merremii 0 1 0 0 1 1 1 1 1 1 1 1 1 1 1 Xenodon nattereri 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 Xenodon pulcher 0 0 1 0 0 0 0 0 0 0 0 1 0 0 0 Xenodon rhabdocephalus 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Xenodon severus 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Xenopholis undulatus 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 Xenopholis werdingorum 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
112 CHAPTER 3
The Role of Seasonal Flooding in Assembling Snake Communities in the
Pantanal and Surrounding Areas
Liliana Piatti1*, Cristiano de C. Nogueira2, Vanda Lúcia Ferreira4, and Marcio Martins2
¹Programa de Pós-Graduação em Ecologia, Instituto de Biociências, Universidade de São
Paulo, São Paulo, São Paulo, Brazil
2Departamento de Ecologia, Instituto de Biociências, Universidade de São Paulo, São Paulo,
São Paulo, Brazil
4Centro de Ciências Biológicas e da Saúde, Universidade Federal de Mato Grosso do Sul,
Campo Grande, Mato Grosso do Sul, Brazil
*Corresponding author
Email: [email protected] (LP)
Short title: Processes assembling snake communities in the Pantanal
113 1 ABSTRACT
2 The assembly communities is simultaneously influenced by factors that are relatively
3 deterministic and more stochastic factors, acting at different spatial and temporal scales. In
4 addition, historical factors often shape the regional pool and can limit the ecological
5 features of species that compose communities. Stressful environments, such as seasonally
6 flooded areas, tend to act as environmental filters, excluding the species unable to deal with
7 hardship imposed by local conditions. When environmental filtering is the strongest force
8 acting to shape species assemblages, species with similar niches co-occur in local
9 assemblages more frequently than expected by chance. Herein we used phylogenetic and
10 trait-based approaches to provide insights on whether seasonal flooding acts as an
11 environmental filter on snake communities in the Pantanal floodplain and in other
12 seasonally flooded areas of the Paraguay River Basin (PRB). We analysed the phylogenetic
13 and phenotypic structure of local communities located on different areas in the basin and
14 investigated how they are related to gradients of forest cover and flood intensity. Contrary
15 to our predictions, evidence of environmental filters found in PRB were not related to
16 flooding gradients, but could be correlated to the forest cover gradient. More forested areas
17 in the PRB had lower species richness and showed morphological convergence, but they did
18 not show lower relative functional diversity when compared to open areas. Thus, historical
19 divergences among the regional pool of different communities may also have given rise to
20 the convergences observed, rather than an isolated action of environmental filtering. The
21 increase in flooding was related with an increase in the occurrence of aquatic and generalist
22 species in the local assemblages. Besides promoting the dispersion of aquatic species,
23 flooding could be decreasing the relative force of deterministic processes (mainly
24 competitive interactions) on community assembly and favouring generalist habits by
25 promoting recurrent ecosystem disturbances.
114 26 INTRODUCTION
27 The structure of biological communities is shaped by historical and ecological factors
28 [1]. On larger scales of time and space, processes of speciation and dispersal provide species
29 pools for the composition of communities [2, 3, 4], and the organization of these
30 communities from regionals pools is influenced by a set of forces acting at narrower scales
31 [5, 6, 7, 8]. One of these ecological forces currently supposed to shape communities is
32 environmental filtering [9, 10, 11]. The environment in which the community is established
33 is considered as a filter selecting a species subset from a regional pool on the basis of
34 features that increase the fitness in that area, and excluding taxa that do not have the
35 appropriate ecologically relevant traits, resulting in communities composed of similar
36 species and convergent patterns [12, 13, 14]. Competition for resources is another force
37 acting on community structures [9, 15, 16]. Species with similar resource use are prevented
38 to co-occur in communities under limited resources due to limiting similarity, producing
39 patterns of divergence between species at local assemblages [17, 18]. Alternatively,
40 communities can be formed randomly from the source pools when neutral processes are the
41 more dominants forces driving the composition of local species assemblages [19, 20, 21].
42 For Neotropical snakes, the imprint of historical differences among the lineages
43 present at regional pools emerges as a primary force molding the phylogenetic composition
44 and ecological characteristics of the local assemblage [22], since community features are
45 largely a function of the proportional representation of different ancestral lineages [22, 23].
46 However, recent studies suggested that ecological interactions could also act in the
47 assembly of snake communities that exhibit trait divergence or convergence correlated with
48 environmental gradients [24, 25]. Overall, both evolutionary history and ecological
49 interactions may, simultaneously or not, act to determine community structure [23, 24, 26,
50 27]. The challenge remains in identifying the relative contribution of these two major forces
51 in different organisms, regions, and traits [28,29].
115 52 Traits that mediate either environmental filtering or limiting similarity may or may
53 not be phylogenetically conserved [30] and their evolution may or may not be associated
54 with differential resource use [31, 32]. Identifying specific traits related to either
55 environmental filtering or limiting similarity can provide important insights about the
56 processes involved and their relative importance to the assembly of communities [33].
57 Ecomorphology the relationship between morphology and ecology links the functional
58 design of organisms with their environment, and it is clear that a combination of recent
59 ecological mechanisms and phylogenetic history determine organismal design [34, 35]. For
60 snakes, both habitat use and prey type, which represent almost the entire fundamental
61 niche of species [36], correlates strongly with morphology and were not always conserved
62 throughout the evolution of lineages [22, 37, 38]. For example, morphological shifts
63 associated with arboreality or fossoriality have occurred independently, in phylogenetically
64 unrelated species, suggesting an ecological origin, whereas specific morphologies
65 widespread in species of a given clade suggest much older origins in the evolutionary history
66 of a particular group [39, 40, 41]. Regarding diet, experimental studies have shown that
67 snakes generally exhibit precise, genetically-determined, species-specific preferences for
68 some prey types [42, 43], but diets of advanced snakes have diverged repeatedly during the
69 evolutionary history of snakes, what also suggest a role of ecological process on it [37]. The
70 interplay between historical and ecological processes acting on community assembly can be
71 studied by characterizing the phenotypic structure of communities, which describe the
72 ecomorphological patterns of species resulting of the interactions of the phylogenetic
73 features of organisms with the environments to which they are exposed [6, 11, 16, 33].
74 A number of recent studies have argued that stressful environments, such as
75 seasonally dry or seasonally flooded areas tend to exhibit low phylogenetic diversity because
76 they act as habitat filters, excluding the species unable to deal with hardship conditions [44,
77 45]. Specifically to flooding, studies point out that its adverse effects are responsible for
116 78 changes in distribution and species composition for several taxa [46, 47, 48, 49, 50] and that
79 different flow regime parameters (such as flooding frequency, duration, and predictability)
80 can affect how organisms adapt or fail to adapt to flooding [46].
81 The largest continuous tropical floodplain in the world is the Pantanal, located in
82 central South America, in the depression of the Paraguay River basin (PRB). This basin
83 encompasses other seasonally flooded areas subject to annual flood pulses, which vary in
84 intensity and duration according to rainfall patterns along the entire basin [51]. In the
85 Pantanal, the complex vegetation cover and highly seasonal productivity support an
86 abundant fauna, composed of species from different ecoregions that surround this
87 floodplain [49, 52]. Nevertheless, the Pantanal biota is poorer than the biota of surrounding
88 regions [52], a pattern that may stem from its recent formation – 2,5 Ma, during the last
89 Andean orogeny phase [53] – and the ecological hardships imposed on organisms due to the
90 flood pulse regimes [49, 52, 54].
91 Herein, our main goal was to test the role of the seasonal floods in the Pantanal
92 floodplain, and in other flooded areas of PRB, as an environmental filter for snake
93 communities. To do that, we investigated the relationships of phylogenetic and phenotypic
94 structure of mecommunities in the Paraguay River Basin and the gradient of flooding where
95 these metacommunities are located. Because the gradient of forest cover also proved to be
96 important to the assembly of Neotropical snakes communities [24], we additionally tested
97 for a relationship between the structure of communities and this environmental feature. As
98 community assembly forces can act simultaneously on different dimensions of the
99 fundamental niche of species and in varied directions [55], we used phenotypical diversity
100 patterns, considering different sets of species traits, to help detect and explain evidences for
101 non-random processes [31].
102 Our expectations were: if flooding acts as an environmental filter on communities,
103 (1) the Pantanal floodplain would show a phylogenetically clustered species composition
117 104 compared to the pool of species from the whole basin; (2) local communities distributed
105 across the basin would show a phylogenetic aggregation positively related to the local
106 amount of flooded area; (3) metacommunities from areas with similar levels of flooding
107 would show similar phenotypic structure, what means they will be composed of species with
108 similar morphologies related to similar habitat use. We also expected that (4) the gradient of
109 forest cover would influence community structure independently of the flood gradient and
110 (5) the functional diversity of communities would be lower in cases where they are
111 assembled by environmental filters.
112 MATERIALS AND METHODS
113 Study area
114 The Paraguay River basin (PRB) is located between 14° and 27° S and 53° and 67° W.
115 The entire catchment area covers 1,135,000 km2, which are distributed along mostly of
116 Paraguay and parts of Bolivia, Brazil and Argentina. The basin includes a number of
117 terrestrial ecoregions (sensu [56]): Cerrado savannas, Chiquitano dry forest, Bolivian
118 montane dry forest, Dry Chaco, Humid Chaco, Alto Paraná Atlantic forest, Central Andean
119 Puna, Southern Andean Yungas and the Pantanal (Fig 1). The basin is strongly affected by
120 rivers and river pulses, enclosing 13 sites considered “Wetlands of International Importance”
121 by the Ramsar convention [57], in addition to the Pantanal, which is the largest continuous
122 tropical wetland in the world.
123 The Pantanal is a floodplain covering an area of about 140,000 km2 situated in the
124 upper Paraguay River depression. The area is subjected to an annual, predictable,
125 monomodal flood pulse [58]. During the rainy season (November–March), the vast plain
126 stores water and releases it slowly to the lower sections of the Paraguay River during the dry
118 127
128 Figure 1. Map showing the limits of Paraguay River Basin (outer black line) and its flooded 129 areas, the ecoregions that it encompasses and the Pantanal (internal blue line). The 31 130 dotted boxes are the 0.5x0.5 degree cells used to delimit snakes metacommunities.
131 season (April–October). Flood intensity varies, but, on average, about one-third of the
132 Pantanal fills up each year, with estimates of total area flooded ranging from 10 to 70% of
133 the entire Pantanal depression [58]. Because of the slight declivity of the terrain (2 to 3 cm
134 per km from north to south, and 5 to 25 cm from east to west) floodwaters take about four
135 months to run through the entire Pantanal. The annual variation in intensity and duration of
136 floods depends on the amount of rain and evapotranspiration rates of the floodplains and at
119 137 surrounding headwaters [58]. The vegetation is a mixture of plant communities from the
138 surrounding biomes: moist forests from the Amazon basin and the Atlantic forest, Cerrado
139 savannas from central Brazilian uplands, and dry and wet Chacoan savannas from Bolivia
140 and Paraguay [59]. The limits of the Paraguay basin and the Pantanal adopted here follow
141 Petry et al.[60] and Hamilton et al. [58], respectively.
142 Species distribution data
143 Our analysis was based primarily on two different presence-absence matrices. To
144 construct them, we obtained species distribution data from around 6500 georeferenced
145 snake records from localities across the Paraguay River basin. Records were gathered either
146 by revision of voucher specimens in zoological collections (about 60% of the records) or by
147 the compilation of reliable literature records (around 25%). The database was completed
148 (another 15%) with unpublished data obtained from management plans of protected areas,
149 unpublished technical reports of environmental impact studies, and with original data from
150 partner researchers, whenever they could be confirmed through examination of voucher
151 material. Records of undescribed species or specimens not identified to the specific level
152 were excluded. Geographical coordinates were obtained from the original record or by
153 contacting the original collectors whenever possible. Alternatively, they were obtained after
154 visual inspection using Google Earth 7.1, considering the localities described in the original
155 record. If detailed information on localities was also lacking, we used the municipality
156 centroids. All species records are detailed in Pimentel et al. no prelo [61]. Snake taxonomy
157 followed Zaher et al. (2009) [62], Grazziotin et al. (2012) [63], Jadin et al. (2014) [64] and
158 Hedges et al. (2014) [65].
159 The first matrix derivated from gathered data described the occurrence of snakes
160 from the whole Paraguay River Basin (all records gathered) and inside the limits of the
161 Pantanal floodplain. This matrix was used to calculate the phylogenetic structure of the
162 snake fauna of the entire Pantanal floodplain compared to that of entire basin.
120 163 The second matrix described the presence-absence of snakes in different areas of
164 the PRB. It was used to describe and investigate the relationship of the phylogenetic and
165 functional structures of communities with forest and flooding gradients where they were
166 placed in the basin. To construct this matrix, we superimposed the species occurrence
167 dataset on a grid of 0.5 x 0.5 degree cells that covered the entire basin. We then considered
168 the species recorded within each grid cell to comprise a separate snake metacommunity. For
169 our analysis we used the 31 best sampled of the 510 metacommunities delimited by grid
170 cells in the PRB area. These metacommunities were chosen because they showed species
171 richness compatible with the knowledge about snake diversity in that specific area. That is,
172 we compared the communities of these grid cells with studies from the same ecoregions
173 and similar latitudes (e.g., Strüssmann and Sazima 1993 [66], Leynald and Bucher 1999 [67],
174 Souza et al. 2010 [68] Bellini et al. 2015 [69]) and only included a cell in our analysis if the
175 species richness was similar to that found in those studies. As a result, we only used
176 relatively well-sampled communities to analyse species turnover. In this matrix we only
177 included species of advanced snakes (Caenophidia), ignoring records of species of the
178 families Anomalepididae, Aniliidae, and Boiidae, which represented 10.16% of PRB snakes
179 species. Species of the family Anomalepididae that occur in the PRB have a much smaller
180 probability of detection compared to other species and are taxonomically poorly resolved
181 [65]. On the other hand, species of the families Boiidae and Anillidae are rather conspicuous
182 and often they are the first species to be registered at a site, especially when no
183 systematized method is used. In addition, these three taxa are the most ancient of the entire
184 basin, with much older relationships than those among species from other snake families.
185 Considering these facts in conjunction, including species from these clades could cause
186 undesired bias by inflating the phylogenetic variability and other related traits of the
187 metacommunities where they are present, masking the patterns exhibited by other species.
121 188 Environmental data
189 From the 31 grid cells selected because they delimit well sampled
190 metacommunities, we extracted environmental data describing flooding and forest cover.
191 Flooding gradient was quantified based on the percentage of the grid cell that was
192 characterized as seasonally flooding areas, following Lehner and Doll 2004 [70] (Fig 1 in
193 appendix S1). We assumed that the larger the amount of seasonally flooded area within a
194 grid cell, the stronger is the effect of flooding in the metacommunity.
195 The percent of forest cover in a grid cell was calculated based on the sum of the
196 presence of the two land-cover classes (Evergreen and Deciduous Broadleaf Forests)
197 obtained from EarthEnv database [71]. These two classes include virtually all kinds of
198 vegetation cover that can form an arboreal substrate occurring in the PRB area [51] (see
199 Tuanmu & Jetz 2014 [71] for details about each class, and Fig 1 in appendix S1 for their
200 mapping in the PRB). Because we are considering variables that could have acted on species
201 occurrence along their evolutionary history, we corrected the values of Decidous Broadleaf
202 presence for cells located between -21.8 and -23.8 S and -59.2 and -60.7 W. In these cases
203 the current values of vegetation presence was a result of anthropic deforestation in recent
204 decades [72]. Therefore, for these areas, we used the average value found in the eigth
205 surrounding cells.
206 These two variables were not correlated (Pearson correlation coefficient = -0.11, p =
207 0.574) and showed no spatial autocorrelation (Mantel test based in 999 permutations = -
208 0.04, p-value = 0.687). The flooding cover gradient varied between 0 and 100% (mean 36.6,
209 standard deviation 40.71) and the forest cover gradient varied between 3.9 and 76.2 (mean
210 26.9, standard deviation 19.5).
211 Phenotypic data
122 212 Since the use of combined life-history traits has the potential to better elucidate the
213 effect of several processes on communities [31], we tried to assess multiple niche axes of
214 snakes by investigating morphologies related to habitat use and diet.
215 Habitat use
216 We used morphological traits as a proxy for habitat use by snakes. The
217 correspondence among morphological and ecological traits of snakes has been supported by
218 numerous studies [24, 32, 37, 38, 40, 41, 42] and morphology is considered a good surrogate
219 of habitat use, mainly when accurate data on specie habitat use is not available. From the
220 species registered in the 31 well sampled metacommunities, we obtained different
221 morphological characterizations from specimens from zoological collections (the list of
222 specimens used is in appendix S1). We used only adult male specimens, which had no
223 deformation regarding body, tail and head size or shape. For each individual, we measured
224 usual morphometric variables [40, 41]: snout-vent length (SVL), tail length (TL),
225 circumference around midbody (CM), ventral (VS) and subcaudal (SS) scale count, head
226 width and height at eye (HW-E and HH-E) and nostril (HW-N and HH-N) position, height of
227 inferior border of the eye (HE) and nostril (HN) and distance between eyes (DE) and nostrils
228 (DN) (Tab 2 in appendix S1). Body size and tail measurements were quantified using a
229 measuring tape (precision 1 mm). Head size and eye and nostril position were obtained from
230 scanned images of the dorsal and lateral view of the specimens’ head using the ImageJ
231 software 1.46a [73]. Based on these measurements, we created new variables that
232 described the shape of the species, which reflect its habitat use (Table 1). Because we
233 expected allometric relationships for snakes [93,94], before calculating the mentioned rates
234 we conducted an allometry correction: for each individual, size “s” was computed as the
235 geometric mean of all 13 measurements, and each measurement was then divided by “s” to
236 obtain shape ratios. After that, the ratios described above were calculated for each
237 measured specimen, and then scaled by the average value of all specimens. After that,
123 238 Table 1. Morphological traits used as a surrogate for habitat use by Caenophidia snakes species at Paraguay River Basin.
Morphological Measures Indicator of Trends Justification References
RBS There are constraints on the total length of arboreal snakes related with the (Relative body size): SVL/(SVL+TL) Arboreal snakes tend to tradeoff between locomotor ability and effects of gravity on blood have smaller relative circulation. Large-bodied snakes need to support more heterogeneous 74, 75, 76, Relative snout-vent length snout-vent length gravity columns, they can become so heavy as to not be supported by smaller 77, 78, 79 branches, and they may have more difficulty in spanning gaps.
RTS Long tails are an adaptive response to cardiovascular stress on blood (Relative tail size): TL/(SVL+TL) Arboreal snakes tend to circulation imposed by gravity. Also some arboreal snakes can use the tail to 41, 76, 79, Relative tail length larger relative tail length anchor to branches in order to generate forces necessary to bridge gaps. 80
ROB (Robustness): CM/ (SVL+TL) A slender body facilitates locomotion in discontinuous substrates and can Arboreal snakes tend to 40, 81 Robustness increase camouflage in arboreal habitats. Also, small mass/length ratios help have slender bodies to maintain an adequate internal pressure.
DVS There is a 1:1 relationship between number of ventral scales and number of (Density of ventral scales): trunk vertebrae in several snake species. In arboreal habitats, where push VS/SVL points necessary for limbless locomotion occur in three dimensions and Arboreal snakes tend to empty spaces between points must be crossed, natural selection should have higher density of favour snakes with increased flexibility and manoeuvrability. Increases in 75, 82, 83, Density of ventral and sub- ventral and sub-caudal vertebral number can enhance these mechanical characteristics by increasing 84, 85, 86, DSS caudal scales scales the number of articulation points, the amount of muscles involved, and 87 (Density of sucaudal scales): motor control. Moreover, for terrestrial snakes a trade-off may exist SS/TL between speed and manoeuvrability. Individuals with fewer body vertebrae may be faster, but less flexible, than those with more vertebrae.
HSH (Head shape in height): HH-E/HH- Proportionally, aquatic snakes have an enlarged posterior part of the head. Aquatic species tend to N Head shape – ratio between This could reflect a solution to the trade-off between the need for a more have a narrower anterior 88 head height at eye and streamlined head to circumvent the physical constraints of underwater HSW part of the head nostril position displacement without losing the ability to swallow large prey. (Head shape in width): HW-E/HW-N
124 EPH Eye position – ratios (Eye position in height): Aquatic species tend to between distance between A more dorsal position of the eyes allows aquatic species to target prey or to HE/HH-E have dorsally positioned 88, 89, 90, eyes and head width and see predators that are positioned above them. EPW eyes 91 height of inferior border of (Eye position in width): the eye and head height DE/HW-E NPH Nostril position - ratios (Nostril position in height): Aquatic species tend to between distance between HN/HH-N have dorsally positioned Nostrils more dorsally positioned allow aquatic snakes to breathe at the 88, 90, 92 nostrils and head width and NPW nostrils surface of the water while remaining submerged. height of inferior border of (Nostril position in width): nostril and head height DN/HW-N
125 239 average values were obtained for each specie and used in subsequent analyses. The number
240 of specimens used to calculate the average species values varied from 1 to 16 (table 2 in
241 appendix S1).
242 When possible, we took measurements from specimens collected in the PRB or
243 nearby areas. We did not find adult males of Micrurus diana, Phalotris nigrilatus, Philodryas
244 livida, Xenodon nattereri, and Xenopholis undulatus in suitable preservation conditions at
245 the zoological museums we visited. To include these species in our analysis, we collected
246 morphological measurements of them from the literature and also used the values from
247 related species to characterize their shapes. For Micrurus diana, we obtained the values of
248 tail and body size and scale counts from Pires et al. 2013 [95] and used the average values of
249 M. frontalis, a closely related specie, for other measurements. For Philodryas livida, we
250 obtained the values of tail and body size and scale counts from Thomas and Fernandes 1996
251 [97] and used the average values of the closely related P. patagoniensis for other
252 measurements. For Xenopholis undulatus, we obtained the values of scale counts from
253 Jansen et al. 2009 [98] and used the average values of the closely related X. werdingorum for
254 other measurements. We used the average values of Phalotris nasutus to characterize P.
255 nigrilatus and the values of Xenodon dorbigni to describe Xenodon nattereri, based on their
256 phylogenetic relationships.
257 Diet
258 Dietary data was collected from the available literature. The diet of each species was
259 characterized regarding the consumption of eight discrete prey categories: invertebrates,
260 fishes, anurans, reptiles, birds, mammals, and a category grouping caecilians and
261 amphisbaenians (Tab 3 in appendix S1). These categories were used following previously
262 published analyses [22, 23, 99]. We considered only the presence and absence of the
263 categories, and ignored alimentary items that were occasionally registered and do not
264 follow the dietary patterns known to the species.
126 265 Phylogenetic Hypothesis
266 To describe the phylogenetic relationships between the species found at PRB, we
267 used Mesquite 3.1 [100] to assemble by hand a composite phylogeny based primarily on
268 Tonini et al. 2016 [101], as well as collating information from various additional phylogenies
269 (see details in appendix S1). The placement of species that were not included in the
270 published phylogenies was inferred according to the relationships of sister species or
271 included as a polytomy in nodes containing closely related species. Tree branch lengths were
272 calibrated by the BLADJ module of Phylocom 4.1 [102], using clade age estimates provided
273 by Tonini et al. (2016) [101]. Undated nodes were evenly interpolated between dated nodes
274 (Fig 2 in appendix S1).
275 Data Analysis
276 Phylogenetic structure of communities
277 We analysed the phylogenetic structure of the snake fauna of the Pantanal
278 floodplain and of the 31 well sampled metacommunities using the net relatedness (NRI) and
279 nearest taxon (NTI) indices [3]. NRI is more sensitive to tree-wide patterns since it is derived
280 from the mean pairwise distance between all species in a community [3]. NTI is derived from
281 the mean distance separating each species in the community from its closest relative, so it is
282 more sensitive to patterns closer to the tips of the phylogeny [3]. To obtain the significance
283 of observed results we generated null communities by randomizing (999 randomizations)
284 the tip labels of the phylogenetic distance matrix, holding species richness and frequency of
285 occurrence constant in each community and using all the species found in the Paraguay
286 River Basin (PRB) as a regional pool in all cases. Positive NRI and NTI values indicate
287 phylogenetic clustering, whereas negative values indicate phylogenetic overdispersion [3].
288 These analyses were performed using the picante library [103] in R statistical software
289 (v2.15.0; R Development Core Team, 2014 [104]). To evaluate if phylogenetic structure of
127 290 communities (NRI and NTI) was related to environmental gradients we used partial linear
291 regressions [105].
292 Also, to better describe the phylogenetic composition of each of the 31
293 metacommunities, we performed the phylogenetic fuzzy-weighting method developed by
294 Pillar and Duarte (2010) [106], using the package PCPS [106] in the R software [104]. This
295 method uses phylogenetic similarities between taxa to scale-up the phylogenetic
296 relationships to the site level. First, pairwise phylogenetic distances between species were
297 taken from our phylogenetic hypothesis, and then transformed into a phylogenetic similarity
298 matrix. Then, these phylogenetic similarities were used to weigh snake species composition
299 in each cell (metacommunity), using a fuzzy set algorithm (see Pillar and Duarte 2010 [106]
300 for details). This procedure generated a matrix P, of species by metacommunities, containing
301 species composition weighted by phylogenetic relationships. We then performed a principal
302 coordinates analysis (PCoA) on matrix P, based on the square root of Bray–Curtis distances
303 between cells, which generated principal coordinates of phylogenetic structure [108]. Each
304 PCPS represented one orthogonal vector describing gradients of the phylogenetic structure
305 of the metacommunity (grid cell) and indicated which clades were most strongly associated
306 with them [108]. The ordination resulted in 30 orthogonal vectors describing
307 metacommunity gradients. To select only vectors related to the environmental gradients,
308 we adopted the criteria proposed by Duarte et al. 2012 [109], that considers just the subset
309 of orthogonal PCPS expressing the maximum association between phylogenetic structure
310 and a set of explanatory variables of interest (flooding and forest cover). This subset was
311 found by calculating multiple distance-based redundancy analysis [110] relating the
312 orthogonal axes (pcps) to predictors (environmental gradients), successively increasing the
313 number of axes, and observed F-values. The first axes (pcps1) of the PCPS analysis minimized
314 the residual sum of squares when relating phylogenetic composition to explanatory
315 variables, followed by the set pcps1 + pcps4. The independent and shared contributions of
128 316 forest and flooding cover to phylogenetic composition structure were assessed using a
317 partial linear regression [105].
318 The associations between different snake phylogenetic clades and the phylogenetic
319 vector were plotted in an ordination biplot. We also determined and plotted in what
320 ecoregion (sensu [55]) each metacommunity was located. The BRP encompasses different
321 ecoregions, with particular biogeographical histories, which could influence the phylogenetic
322 compositions of local assemblages.
323 Trait convergence/divergence patterns in metacommunities
324 To search for functional diversity patterns resulting from environmental filters or
325 competition processes, we used the general analytical approach described in detail in Pillar
326 et al. (2009) [111] and Pillar and Duarte 2010 [106], assessing trait divergence and trait
327 convergence patterns as a function of environmental features. Briefly, this technique
328 calculates the degree of correlation between a matrix describing the difference of
329 phenotypic traits between pairs of communities and a matrix of environmental features of
330 each community. This method also provides a measure of phylogenetic divergence between
331 communities, indicating how phenotipical patterns are influenced by phylogenetic
332 relationship [106, 111]. Evidence of environmental filtering acting on communities is
333 indicated by trait convergence related to environmental gradients, whereas trait divergence
334 indicates limiting similarity acting on communities [108, 111]. Traits related to habitat use
335 and diet were separately correlated with environment since the forces that shape the
336 structure of metacommunities can act in different ways on different traits [28, 29]. In both
337 cases, only traits that maximized trait convergence/divergence along the environmental
338 gradients were used. These traits were determined through interactive searching using the
339 “optimal” function in the Syncsa R package [112], which also was used to calculate the
340 correlation matrices described above. The significance of correlations between matrices was
341 tested against null models using 999 permutations.
129 342 To help the interpretation of convergence/divergence patterns and describe their
343 relationship to environmental gradients we used linear regressions to explore trends in
344 community-weighted means (CWM, [113]) along forest and flooding gradients. CWM was
345 computed for each metacommunity (grid cell) as the mean of species trait values
346 standardized by the marginal total within the metacommunity [113]. This was calculated for
347 each morphological and dietary trait separately.
348 Functional diversity of metacommunities
349 We estimated the functional diversity of each metacommunity using functional
350 richness index (FRic) and functional dispersion index (FDis) [114], and related them to
351 environmental gradients. These indices were calculated distinctly for morphological and
352 dietary traits, and also separately for traits that maximized convergence/divergence
353 between environments and for the remaining traits. FRic measures the volume of
354 multidimensional functional space occupied by the community [113, 114] and is equivalent
355 to the convex hull volume occupied. When it is calculated based on binary data (as in our
356 diet matrix) FRic is measured as the number of unique trait value combinations in a
357 community [114], which means the number of particular dietary strategies present in the
358 community. Functional dispersion index (FDis) is the mean distance of individual species to
359 the centroid of all species of the community, in multidimensional trait space [114]. In
360 practical terms these indices are complementary, FRic will become greater with the
361 presence of more traits, while FDis measures the differences among functional entities
362 within communities [114]. The values of FRic and FDIs observed were transformed into
363 indices of standardized effect size (SES) (as in Webb 2002 [3]) based on 999 randomizations
364 of the matrix of species presences in communities, maintaining species richness across
365 samples and the trait matrix in its original configuration. Positive values indicate greater
366 functional diversity than the null expectation and negative values indicate lower functional
130 367 diversity than null expectation. We used linear regressions to explore trends in functional
368 diversity along forest and flooding gradients [105].
369 Finally, to assess the extent to which the observed patterns in functional diversity
370 were linked to site-specific factors structuring assemblages, rather than arising by chance
371 from random assembly of the communities we assessed the statistical significance (α = 0.05)
372 of the observed functional indices using the quantile of observed indeces vs. null community
373 indices. We used the FD package [114] for R software [104] to calculate FRic and FDis and
374 the picante [103] package to construct the null models.
375 RESULTS
376 We registered 156 species of snakes in the Paraguay River Basin (PRB) and 83 in the
377 Pantanal floodplain (S3 table). We identified a total of 115 Caenophidian species in the
378 whole basin, and, in metacommunities delimited by the 0.5 degree grid cells distributed
379 across the basin, the Chaenophidian richness varied between 15 to 61 species (Tab 1 in
380 appendix S1) and was negatively correlated only with the forest cover gradient (Pearson
381 correlation coefficient = -0.51, p = 0.003), but not with the flooding gradient (Pearson
382 correlation coefficient < 0.01, p = 0.96).
383 Although there was a tendency for clustering, NRI and NTI indices of the entire
384 Pantanal floodplain were not different from those observed for randomly assembled
385 communities (NRI = 1.27, p = 0.11; NTI = 2.10, p = 0.06), considering the Paraguay River
386 Basin (PRB) as a regional pool. The indices for the 31 metacommunities distributed across
387 the PRB showed that five of them had significantly overdispersed phylogenetic composition
388 (regarding NRI and/or NTI) and all other metacommunities had non-significant values,
389 indicating phylogenetic randomness (Tab 1 in appendix S2). Overdispersed
390 metacommunities considering NRI were located in non-flooded areas dominated by open
391 vegetation (forest cover between 8 and 30%), while overdispersed communities considering
131 392 NTI were widely distributed throughout of flooding and forest cover gradients (Fig 2 and Tab
393 1 in appendix S2).
394 Only NRI was significantly related to environmental variables: mecommunities with
395 more flooded areas showed higher NRI, that is, exhibited a tendency to contain more
396 phylogenetically related species considering oldest relationships between species (Fig 2).
2 397 However, flooding accounted for only 24.8% of the variation in NRI values (R = 0.2484, F1,29
398 = 0.585, p = 0.004).
399
400 Figure 2. Variation in NRI and NTI in relation to the amount of flooded area and forest 401 cover in snake metacommunities of the PRB. Positive values indicate phylogenetic 402 clustering and negative values indicate overdispersion. Values between dashed lines are 403 not statistically significant, indicating phylogenetic randomness. Grey lines show adjusted 404 linear models to the respective environmental gradients, and dotted grey lines show the 405 95% confidence interval of the model.
132 406 The axes of the PCPS analysis described about 35% (27% on the pcps1 and 8% on
407 pcps4) of the phylogenetic composition of the 31 metacommunities distributed across the
408 PRB. The first pcps was positively related to the NRI index (Pearson correlation coefficient =
409 0.78, p < 0.001) and was determined mainly by the presence of species from the tribe
410 Elapomorphini, the genus Xenopholis, and the tribes Echinanterini, Hydrodynastini,
411 Hydropsini, Pseudoboini, Tachymenini and Philodryadini, and negatively with the occurrence
412 of species of the family Viperidae (Fig 3). The fourth axis was related positively to both the
413 aggregation index (NTI: Pearson correlation coefficient = 0.36, p = 0.049 and NRI: Pearson
414 correlation coefficient = 0.49, p = 0.005) and to the presence of species of Xenodontini and
415 Psomophini in the metacommunities and absence of Dipsadini, Imantodini and Elapidae (Fig
416 3).
417 When we analysed the pcps axes separately, the first pcps axis was related to
2 418 environmental gradients (R = 0.311, F1,28 = 6.31, p = 0.005), positively with flood gradient
419 and negatively with forest cover (y=0.013 + 0.001*flooding – 0.0019*forest). The fourth
2 420 PCPS was slightly related with forest cover (R = 0.1272, F1,28 = 4.23, p = 0.048, y= -0.029 +
421 0.001*forest).
422 In general, metacommunities located in the Pantanal ecoregion had more
423 phylogenetic similarities with Cerrado communities. Metaommunities in these two
424 ecoregions showed positives values on the first pcps. Considering the fourth pcps, most of
425 the Pantanal metacommunities had positive values, while Cerrado metacommunities had
426 lower values. The metacommunities from the other ecoregions (Dry and Humid Chaco and
427 Alto Paraná Altantic Forest) were almost totally restricted to negative values on the first
428 pcps.
133 429
430 Figure 3. Scatter diagram of the two principal coordinates of phylogenetic structure (PCPS) 431 of snake species occurring in areas with different amounts of flooding and forest cover. 432 Size of empty circles is relative to the amount of flooding and the size of grey circle is 433 equivalent to the forest cover. Letters near the circles indicate in what ecoregion the 434 metacommunity is located: P – Pantanal, Ce – Cerrado, DCh – Dry Chaco, HCh – Humid 435 Chaco and AtF – Atlantic Forest. Other taxa included the tribe Elapomorphini, the genus 436 Xenopholis, and the tribes Echinanterini, Hydrodynastini, Hydropsini, Pseudoboini, 437 Tachymenini and Philodryandini; Col. g1 includes the genera Chironius, Drymoluber, 438 Mastigodryas, Leptophis and Oxybelis; Col. g2 includes the genera Drymarcon, Pseutes, 439 Spilotes, Simophis.
440 Among the eleven morphological proportions considered, density of ventral scales
441 (DVS), head shape in width (HSW), and nostril position in relation to head height (NPH) and
442 width (NPW) maximized metacommunity convergence and divergence in relation to
134 443 environmental gradients. Metacommunity composition weighted by mean trait values was
444 significantly correlated with environmental gradients (ρ(TE) = 0.377, p = 0.01), indicating
445 morphological convergence at the metacommunity level. We also found trait-divergence
446 patterns related to ecological gradients (ρ(XE.T) = 0.268, p = 0.01). For the four traits above,
447 we found a correlation of phylogenetically structured assembly patterns to trait-
448 convergence assembly patterns (ρ(PT) = 0.624, p = 0.038) (metacommunities that were
449 more similar in terms of phylogenetic structure were also similar regarding their average
450 trait values, causing convergence), but we did not find phylogenetically structured patterns
451 when considering the trait-divergence responses (ρ(PX.T) = -0.364, p = 0.991). Also, we did
452 not find neither a phylogenetic signal at species level (ρ(BF) = 0.066, p = 0.113), nor a
453 relationship between phylogenetic structure and the ecological gradient (ρ(PE) = 0.138, p =
454 0.595). Indeed, morphological convergence and divergence after controlling for the
455 phylogenetic structure of the metacommunities still occurred (ρ(TE.P) = 0.376, p = 0.007 and
456 ρ(XE.P) = 0.412, p = 0.006, respectively) showing that the action of the environmental
457 gradients on the mean traits of metacommunities was, at some level, independent of
458 phylogeny.
459 Metacommunities from areas with higher seasonal flooding contained species with
460 less DVS on average, and with nostrils more dorsally positioned (lower NPW and higher
461 NPH). Conversely, species with higher DVS were found in metacommunities from areas with
462 higher forest cover (Fig 4). Considering these traits, the amount of functional space filled by
463 the metacommunity (SESFRic) and the dispersion of the species in the multifunctional space
464 (SESDis) were positively correlated with the amount of flooding cover and had no
2 465 relationship to the amount of forest (R = 0.171, F1,29 = 5.97, p = 0.021, y= - 0.35 +
2 466 0.0085*flooding cover for SESFRic and R = 0.169, F1,29 = 5.895, p = 0.022, y= - 0.43 +
467 0.0104*flooding for SESDis), indicating that areas with more flooding had metacommunities
468 with more functional diversity (Fig 1 in appendix S2).
135 469
470 Figure 4. Relationships of community-weighted ecomorphological means to environmental 471 gradients. Closed black dots and grey line show the relationship to flooding gradient and 472 open dots and dashed line show the tendencies regarding the forest cover gradient. 473 Coefficient f: flooding cover; coefficient c: forest cover.
474 Considering the other seven morphological traits describing the shape of species
475 (RBS, RTS, ROB, DSS, HSH, EPH and EPW), the environmental gradients were not correlated
2 476 with amount of functional space filled by the metacommunity (R = 0.126, F1,29 = 2.065, p =
477 0.146) but the dispersion of species in that space decreased in communities from areas with
2 478 greater flooding cover area (R = 0.311, F1,29 = 13.08, p = 0.001, y= 0.35 - 0.0108*flooding -
136 479 Fig 1 in appendix S2). Thus, metacommunities more affected by seasonal floods were
480 composed of species more similar in the morphological traits cited above. Metacommunities
481 from areas with higher cover of seasonal flooding contained species with lower density of
482 subcaudales scales (R2 = 0.311, p = 0.005), smaller distance between eyes (R2 = 0.201,
483 p=0.011), and an enlarged posterior part of the head (R2 = 0.123, p = 0.052). Communities
484 from areas with higher forest cover contained species with higher robustness (R2 = 0.147, p =
485 0.033) and higher density of subcaudal scales (R2 = 0.311, p = 0.005). These relations can be
486 visualized in Fig 3 in appendix S2.
487 When we investigated the metacommunities regarding diet, the consumption of
488 invertebrates, caecilians and amphisbaenians, snakes, and mammals maximized
489 metacommunity convergence in relation to environmental gradients. In this case we did not
490 find clear patterns of convergence/divergence related to the environment. We found just a
491 weak tendency for trait-convergence assembly patterns (ρ(TE) = 0.291, p = 0.07), that
492 decrease after controlling for the phylogenetic structure of the metacommunity (ρ(TE.P) =
493 0.260, p = 0.08). Metacommunities that were more similar in terms of phylogenetic
494 structure were also similar regarding their average trait values causing convergence (ρ(PT) =
495 0.493, p = 0.011), and we found phylogenetic signal at the species level (ρ(BF) = 0.136, p =
496 0.002) regarding the consumption of the prey mentioned above.
497 Metaommunities from areas with higher seasonal flooding cover had a greater
498 frequency of species preying on snakes and a lower frequency preying on mammals. And the
499 increase of forest cover was related to the decrease of the frequency of species consuming
500 invertebrates (Fig 5).
501 Forest and flooding cover were not correlated neither with the amount of dietary
2 502 strategies used by the species (R = 0.139, F1,29 = 2.25, p = 0.124), nor with the dispersion of
503 the species in the multidimensional space of consumption of invertebrates, caecilians and
2 504 amphisbaenians, snakes, and mammals (R = 0.109, F1,29 = 1.7, p = 0.2 - Fig 2 in appendix S2).
137 505
506 Figure 5. Relationship of environmental gradients and of consumption frequencies of 507 dietary items by species from metacommunities. Closed black dots and grey lines show the 508 relationship to flooding gradient and open dots and dashed lines show the tendencies 509 regarding the forest cover gradient. Coefficient f: flooding; coefficient c: forest.
510 Concerning the other four prey categories, the amount of dietary strategies used by
511 the species in metacommunities was again uncorrelated with environmental gradients (R2 <
1 512 0.001, F ,29 = 0.008, p = 0.992) whereas the dispersion in the multidimensional space of prey
513 consumed showed a tendency to decrease following the increase in flooding cover (R2 =
514 0.135, F1,29 = 4.53, p = 0.042, y= 0.26 - 0.0108*flooding cover, Fig 2 in appendix S2). Species
515 that consumed fishes and anurans occurred with more frequency in areas with more
516 flooding cover (R2 = 0.143, p = 0.036, and R2 = 0.173, p = 0.02, respectively), whereas the
138 517 occurrence of snakes that prey on lizards and birds tended to decrease in those areas (R2 =
518 0.264, p = 0.003, and R2 = 0.177, p = 0.06, respectively). Forest cover did not significantly
519 affect the frequency of consumption of these prey (Fig 4 in appendix S2).
520 Eleven metacommunities showed SESFRic or SESFDis values differing from the null
521 models considering morphological and dietary traits. Besides the tendencies mentioned
522 above, these metacommunities where placed along the whole flooding and forest gradients
523 (Tab 1 in appendix S2).
524 DISCUSSION
525 Flood disturbance has been reported as an important driver of diversity patterns in
526 floodplain plants and animals in different regions [47, 50, 115, 116]. In the Paraguay River
527 Basin (PRB), as in other Neotropical regions [115], seasonal flooding seems to play a role on
528 snake species turnover between flooded and non-flooded areas [117]. However, in our
529 study we failed to find a clear pattern that could support flooding acting as an
530 environmental filter. The snake fauna of the Pantanal floodplain showed only a weak trend
531 toward being composed by closely related species, primarily considering recent
532 relationships. Biotic filters are frequently detected in broader scales because, in general, at
533 these scales environments diverge in current ecological conditions and also biogeographic
534 history and, in consequence, have divergent biotas [8, 118]. The Pantanal snake fauna is a
535 subset of the highly diverse pool present in the PRB and originated in different ecoregions
536 that the basin overlap, and the tendency of phylogenetic clustering may reflect the subset of
537 biotic and abiotic conditions that the Pantanal is subject to when compared to the entire
538 basin area, rather than reflect just the difference in flooding influences. On smaller spatial
539 scales, local communities that showed phylogenetic structure indicating a non-random
540 assemblage process were overdispersed, rather than clustered as expected under the
541 influence of habitat filters and the occurrence of phylogenetic conservatism, and were
139 542 located mostly in non-flooding areas. However, by analysing the phenotypic structure of
543 communities in different niche dimensions combined with phylogenetic structure, we were
544 able to find some evidence of non-random factors acting on the assembly of the PRB snake
545 metacommunities, as we found a significant pattern of phenotypical convergence and
546 divergence linked to environmental differences at the metacommunity level.
547 However, our results also indicate the effects of historical contingencies on the
548 assemblage of PRB local communities. The ordination by phylogenetic structure (Fig 3)
549 resulted in metacommunities grouped by ecoregions (sensu Olson et al 2001 [56]), which
550 means that phylogenetic composition of metacommunities may be largely influenced by the
551 history of the regional pool [119]. This result agrees with previous studies that highlight the
552 influence of neighbouring environments on some communities from central South America
553 [120]. The PRB is a meeting point of regions with divergent origins and divergent current
554 environmental conditions [51]. In order to stochastic or deterministic (niche-based)
555 processes shape communities at small scales, species have to be available in the regional
556 pool and divergent pools would respond differently to the same processes due to
557 evolutionary constraints and histories [119]. Then, the evolutionary histories of the regional
558 pool can have produced the patterns of phylogenetic similarities observed in
559 metacommunities at the PRB and also influenced assembly process that resulted in the
560 other community patterns we found at smaller scales.
561 Contrary to our predictions, evidences of environmental filters (morphological
562 convergence at the metacommunity level even after controlling for the phylogenetic
563 structure of metacommunities) found in PRB were not related to flooding gradients, but at
564 some level could be correlated to the forest cover gradient. But, rather than having a higher
565 frequency of arboreal species than open areas as already reported for other snake
566 communities from South America [24], forested areas at the PRB have lower species
567 richness than open areas and lower frequency of species usually found in open areas
140 568 elsewhere in the PRB. This could reflect a restriction imposed by forested areas on species
569 adaptated to open habitats.
570 It is not surprising to find lower richness in more forested areas at PRB considering
571 that a large part of the region is historically linked to open areas. The PBR is located almost
572 entirely at the ‘diagonal of open formations’ [121, 122], a continuum of relatively open
573 ecoregions running from northeastern Brazil to south-central South America (Catinga,
574 Cerrado, and Chaco). Animal communities from this region currently show a high
575 dominance of open habitat species [23, 123, 124], with some of them probably originating
576 from diversification processes in situ [125]. These species are generally adapted to habitats
577 that tend to be drier, hotter or more exposed to direct sunlight and/or are subject to higher
578 ranges in these conditions than forest habitats. Thus, climatic conditions in forests may
579 constrain the occupation of these habitats by most open-habitat species that compose the
580 PRB species pool, resulting in lower snake richness in regions with higher forest cover. For
581 instance, forests provide a lower availability of adequate microhabitats for thermoregulation
582 [117, 126, 127, 128]). Furthermore, at the PBR, forest patches are restricted, discontiguous,
583 and isolated in a matrix dominated by open landscapes, what also might constrain the
584 diversity and distribution of forested adapted snakes that probably have originated in
585 adjacent forested ecoregions (e.g. Amazon Forest, Atlantic Forest and Andean Yungas).
586 Despite evidence for habitat filtering, functional diversity constrained by the
587 richness of local communities (SESFRic and SESFDis) was not different between areas with
588 different forest cover, a pattern that would be expected as a result of habitat filtering. Thus,
589 we are unable to totally refute the hypothesis that convergent patterns of metacommunities
590 of more forested areas originated through non-deterministic processes, such as the
591 historical contingences mentioned above. Even considering that the morphological
592 convergence pattern was significant after controlling for the phylogenetic structure of
593 metacommunities (ρ(TE.P) = 0.376, p = 0.007), the high correlation of phylogenetically
141 594 structured assembly patterns to trait-convergence assembly patterns (ρ(PT) = 0.624, p =
595 0.038) also point to the interplay between environmental filter and historically mediated
596 stochastic processes acting to sort the composition of metacommunities.
597 Although habitat filtering could have acted over a forest cover gradient, many of the
598 significant changes in morphology occurred along the flooding gradient, as indicated by the
599 values of species traits that characterized the metacommunities. In general, our results point
600 to an expected increase in the occurrence of species with aquatic habitats (e.g, Hydrops
601 caesurus, Pseudoeryx plicatilis) in local communities more subjected to seasonal flooding.
602 Mean morphological values in these assemblages reflected a larger frequency of species
603 with more dorsally positioned eyes and narrower anterior part of the head. Aquatic snakes
604 have an enlarged posterior portion of the head to circumvent the physical constraints of
605 underwater displacement without losing the ability to swallow large prey, and the dorsal
606 position of the eyes and nostril allow aquatic species to target prey or to see predators that
607 are positioned above them, while keeping their bodies submerged [88, 130]. Changes in
608 mean trait values not directly associated to aquatic habitats were also detected with the
609 increased influence of flooding in metacommunities (e. g., decrease in ventral and subcaudal
610 density and more laterally placed eyes). This may be a result of the tendency of
611 metacommunities from flooded areas to have also generalist species, in which
612 morphological adaptations to specialized habits are absent. Additionally, the morphological
613 dissimilarity among species within communities (FDis) from flooded areas was higher than
614 non-flooded areas considering traits that maximized the associations to environmental
615 gradients (DVS, HSW, NPH and NPW, Fig 1 in appendix S2). This probably caused the
616 divergent pattern found at the metacommunity level (ρ(XE.T) = 0.268, p = 0.01). This means
617 that the increased occurrence of aquatic species bearing a typical aquatic morphology not
618 only changed the mean values of morphological ratios, but also produced divergent patterns
619 because they co-occurred with species that use different habitats. However, when other
142 620 morphological traits (RBS, RTS, ROB, DSS, HSH, EPH and EPW) are considered,
621 metacommunities from areas under higher flooding influence were composed by species
622 with more similar morphologies in the multidimensional trait space (smaller FDis, Fig 1 in
623 appendix S2). This also agrees with the hypothesis that areas under higher flooding influence
624 have snake metacommunities with a higher frequency of generalist species than those from
625 areas under lower or no flooding influence, and that seasonal flooding constrain certain
626 specialist habits (e.g. fossoriality).
627 It was expected and supported by our results that the seasonal flooding favours the
628 establishment of aquatic species in local communities. Recurrent flooding, in addition to
629 promoting the dispersion of riparian or aquatic taxa [61, 69], provides essential conditions
630 for aquatic species and seems to increase their frequency in local communities. The rarity of
631 species from typically aquatic clades in non-flooded areas and their higher frequency in
632 flooded areas should have originated the positive relationship between the percentage of
633 flooding area and net relatedness index (NRI). This relationship occurred with the index
634 starting at negative values (overdispersion of communities) in non-flooded areas and
635 reaching null values in areas with high flooding influence, rather than varying between the
636 extremes of NRI (overdisperded and clustered values). The increased frequency of diets
637 including fish and anurans as prey in assemblages more subjected to flooding (Fig 4 in
638 appendix 2) also seems to be a consequence of the higher frequency of aquatic species in
639 those areas, since fish and anurans are the main prey of aquatic and semiaquatic snakes
640 [88].
641 But the role of flooding on the assemblage of PRB snake communities can go beyond
642 the dispersion and succesfull occupation of aquatic species. We believe that flooding can
643 decrease the relative force of deterministic processes that could also affect the assembly of
644 comunities (e. g., competitive interactions) as it frequently disturbs the ecosystem. Two
645 thirds of the metacommunities from areas with high flooding cover (with 75% or more of
143 646 flooding cover) had random phylogenetic and phenotypic structure. Meanwhile, most
647 metacommunities with significantly overdispersed phylogenetic or phenotypic structure
648 were located in areas with no seasonal flooding (table 1 in appendix S2). Large seasonal
649 water-level changes in tropical floodplains result in continuous disassembly and reassembly
650 of aquatic and wetland communities, due to high exchange rates and redistribution of
651 organisms that temporarily leave and subsequently recolonize the space [131, 132, 133].
652 Thus, it is likely that seasonal flooding favors the dominance of generalist species due to the
653 hypothesis that generalists cope better with environmental changes, primarily because they
654 are apparently able to change between resources [130, 134]. In bird assemblages in the
655 Brazilian Cerrado, the high seasonality and predisposition to disturbance (such as fire and
656 drought) exhibited by open habitats were also indicated as mechanisms selecting for
657 generalist species that adaptively respond to different environmental conditions throughout
658 the year [118].
659 However, some associations between species traits and environmental gradients are
660 not explained by the hypotheses presented above. For example, the unpredicted decrease in
661 the frequency of lizards, birds, and mammals in snake diets from seasonally flooded areas,
662 and the pronounced increase in robustness with an increase in forest cover were against our
663 expectations. These patterns might result from biotic features that were not considered in
664 our analysis (e.g. a lower availability of lizards, birds and mammals in flooded areas may
665 have caused the observed decreased consumption of these items) and/or from conserved
666 traits. Additionally, we failed to find a clear pattern between functional diversity and flood
667 or forest gradients when we compared observed values with null models (Tab 1 in appendix
668 S2). Thus, the hypotheses that functional diversity patterns result from stochastic or
669 antagonistic forces acting on local scales can not be rejected completely. Moreover,
670 although significant, the relationships between community structure and the environmental
671 parameters considered showed low correlation values. This result indicates that flooding
144 672 and forest cover were not the only factors that have affected the assembly of PRB snake
673 communities.
674 Despite the limitations mentioned above, we provided evidence that habitat
675 filtering can be acting through the forest cover gradient in addition to historical
676 contingences, and that seasonal flooding can increase the importance of neutral processes
677 on the structure of snake communities in the Pantanal and the PRB. Future research using
678 detailed and systematic biological sampling and assessing environmental conditions in more
679 detailed ways and smaller scales could provide stronger evidence for the processes
680 mentioned here. A better understanding of the causal relationships between species traits
681 and fitness would also be useful. However, as these approaches are more challenging and
682 the knowledge about emergent community patterns of the Pantanal and their origins are
683 still scarce, the descriptive approaches used herein provided useful indications of the
684 mechanisms affecting community assembly in this recent ecoregion, as well as a set of
685 quantitative hypotheses to be addressed and extended to other areas with seasonal
686 flooding.
687 ACKNOWLEDGMENTS
688 For give us access to the zoological collections under their care or for assist the 689 gathering of the morphological data we thanks Gustavo Graciolli, Thomaz R.F. Sinani, Iris 690 Stefano, Felipe F. Curcio, Jacqueline Pimentel, Frederick Bauer, Andrea W. de Albertini, 691 Humberto Sanchés, Katia A. Wood, Nicolas Martinez, Diego B. Villafañe, Hugo Cabral, Jorge 692 D. Williams, Leandro Alcalde, Julian Faivovich, Santiago J. Nenda, Esteban O. Lavilla, Sonia Z. 693 Kretzschmar, Blanca Alvarez, Soledad Palomas, Hussam Zaher, Alberto B. Carvalho, Giuseppe 694 Puorto and Valdir G. Germano. We are most grateful to Marcus Cianciaruso and Hamanda B. 695 Cavalheri for useful comments and insights, and Hannah Doerrier for the English revision of 696 earlier drafts of the manuscript. V.L.F. thanks FUNDECT (187/14), for partial financial 697 support. C.N.N. thanks CNPq and FAPESP (2012/19858-2) for postdoctoral fellowships. The 698 authors declare no conflict of interest.
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1042 SUPPORTING INFORMATION
1043 S1 Appendix. Description of Paraguay River basin (PRB) regarding flooding and forest
1044 cover, occurrence of snakes species at PRB and phylogenetic and phonotypic
1045 characterizations of species.
1046 S2 Appendix. Values of phylogenetic and functional structure of snake metacommunities,
1047 and additional relations of communities’ functional diversity with environmental gradients
1048 at Paraguay River basin (PRB).
156 1049 SUPPORTING INFORMATION - The role of seasonal flooding in assembling snake communities in the Brazilian Pantanal and surrounding 1050 areas
1051 S1 Appendix. Description of Paraguay River basin (PRB) regarding flooding and forest cover, occurrence of snakes species at PRB and phylogenetic and
1052 phonotypic characterizations of species.
1053 Figure 1. Maps showing the limits of Paraguay River basin, Pantanal floodplain, local communities and the distribution of seasonal flooded areas, evergreen 1054 broadleaf forest and deciduous broadleaf forest.
157 1055 Table 1. Species presence-absence table in the 31 0.5 x 0.5 degree grid cell delimiting metacommunities at Paraguay River Basin. The first two lines indicate 1056 coordinates of the centroid of each cell. The column PTN indicate which species occur at the Pantanal floodplain.
PTN 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 Long -56.0 -57.0 -56.0 -55.5 -57.5 -56.5 -57.0 -56.5 -55.0 -57.5 -59.5 -60.5 -57.5 -56.5 -59.5 -57.5 Lat -15.1 -15.6 -15.6 -15.6 -16.1 -16.1 -16.6 -16.6 -17.6 -18.1 -18.6 -19.1 -19.1 -19.1 -23.1 -19.6 Amerotyphlops brongersmianus 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Amerotyphlops reticulatus 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Anilius scytale 0 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 Apostolepis ambiniger 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Apostolepis assimilis 0 0 0 1 1 0 0 0 1 1 0 0 0 0 0 0 0 Apostolepis dimidiata 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Apostolepis intermedia 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Apostolepis nigroterminata 0 0 0 0 0 0 0 0 1 0 1 0 0 1 0 0 0 Apostolepis vittata 0 1 0 1 1 1 0 0 0 0 0 0 0 0 0 0 0 Atractus albuquerquei 0 0 1 0 0 1 0 0 0 1 0 0 0 0 0 0 0 Atractus paraguayensis 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Atractus reticulatus 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Atractus thalesdelemai 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Boa constrictor 1 1 0 1 1 1 1 0 0 1 1 0 0 1 0 0 1 Boiruna maculata 1 1 0 0 0 0 1 0 0 0 0 0 0 1 1 0 0 Bothrops alternatus 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Bothrops diporus 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Bothrops jararaca 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Bothrops jararacussu 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Bothrops mattogrossensis 1 1 0 1 0 1 1 1 1 0 0 1 1 1 1 0 1 Bothrops moojeni 1 1 1 1 1 1 1 0 0 1 1 0 0 1 0 0 1
158 PTN 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 Bothrops pauloensis 1 1 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 Chironius bicarinatus 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Chironius exoletus 1 1 1 1 1 1 0 0 0 0 1 0 0 1 0 0 0 Chironius flavolineatus 1 1 1 1 0 1 1 0 0 1 1 1 0 1 1 0 0 Chironius fuscus 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Chironius laurenti 1 0 1 1 0 0 1 1 1 0 1 0 0 1 0 0 1 Chironius quadricarinatus 1 1 0 1 1 1 1 1 0 0 0 0 0 1 0 1 1 Chironius scurrulus 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Clelia clelia 1 0 0 0 0 0 0 1 0 1 0 0 0 0 1 0 0 Clelia langeri 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Clelia plumbea 1 1 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 Corallus hortulanus 0 1 1 1 1 0 0 0 0 0 0 0 0 1 0 0 0 Crotalus durissus 1 1 1 1 1 1 1 0 0 1 1 1 1 1 1 1 1 Dipsas catesbyi 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Dipsas indica 0 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 Drepanoides anomalus 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Drymarchon corais 1 1 1 1 0 1 1 0 1 0 1 1 0 1 0 1 0 Drymoluber brazili 0 1 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 Epicrates alvarezi 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Epicrates cenchria 0 0 0 1 0 0 0 0 0 1 0 0 0 0 0 0 0 Epicrates crassus 1 1 1 1 1 1 1 0 0 1 1 0 0 1 0 0 1 Epictia albipuncta 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Epictia munoai 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Epictia vellardi 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Erythrolamprus aesculapii 1 1 0 1 0 0 1 0 0 1 0 0 0 0 0 0 0 Erythrolamprus albertguentheri 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
159 PTN 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 Erythrolamprus almadensis 1 1 1 1 1 1 1 0 0 0 1 0 0 0 1 0 0 Erythrolamprus frenatus 1 0 0 0 0 0 0 0 0 1 0 0 0 0 1 0 0 Erythrolamprus jaegeri 1 0 0 0 0 0 0 0 0 0 1 0 0 1 1 0 0 Erythrolamprus maryellenae 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Erythrolamprus miliaris 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Erythrolamprus poecilogyrus 1 1 0 1 1 1 1 1 1 1 1 0 0 1 1 1 1 Erythrolamprus reginae 1 1 1 1 1 1 1 1 1 1 1 1 0 1 0 0 0 Erythrolamprus sagittifer 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 Erythrolamprus semiaureus 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 Erythrolamprus taeniogaster 0 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 Erythrolamprus typhlus 1 1 0 0 0 0 1 0 0 0 1 0 1 1 1 0 0 Eunectes murinus 1 1 1 1 1 0 0 0 0 1 0 0 0 1 0 0 1 Eunectes notaeus 1 0 0 0 0 1 1 0 0 0 1 0 0 1 1 1 1 Helicops angulatus 1 1 0 1 1 1 0 0 0 1 0 0 0 0 0 0 1 Helicops infrataeniatus 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 Helicops leopardinus 1 0 1 1 0 1 1 1 1 0 1 0 0 1 1 1 1 Helicops modestus 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Helicops polylepis 0 1 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 Hydrodynastes bicinctus 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Hydrodynastes gigas 1 1 0 1 1 1 1 0 0 0 1 0 0 1 1 1 1 Hydrops caesurus 1 0 0 0 0 0 0 1 1 0 1 0 0 1 0 0 0 Imantodes cenchoa 0 1 0 1 1 0 1 0 0 0 0 0 0 1 0 0 0 Leptodeira annulata 1 1 1 1 1 1 0 0 0 0 1 1 0 1 1 1 1 Leptophis ahaetulla 1 1 1 1 1 1 1 1 1 1 1 0 0 1 1 1 1 Liotyphlops beui 0 1 0 0 1 0 0 0 0 1 0 0 0 1 0 0 0 Liotyphlops ternetzii 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0
160 PTN 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 Lygophis dilepis 1 0 0 1 0 0 1 0 0 0 0 0 0 0 0 1 1 Lygophis flavifrenatus 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Lygophis meridionalis 1 1 1 1 0 1 0 0 0 1 0 1 0 0 1 0 0 Lygophis paucidens 0 1 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 Mastigodryas bifossatus 1 1 1 1 1 1 1 0 0 1 0 0 0 1 1 1 1 Mastigodryas boddaerti 1 1 1 1 0 1 0 0 1 0 1 0 1 1 0 0 0 Micrurus altirostris 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Micrurus annellatus 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Micrurus baliocoryphus 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 Micrurus corallinus 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Micrurus diana 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 Micrurus frontalis 1 1 1 1 1 0 0 0 0 1 0 0 0 0 1 0 0 Micrurus lemniscatus 1 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 Micrurus paraensis 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Micrurus pyrrhocryptus 1 0 0 0 0 0 0 0 0 0 1 0 1 1 0 0 1 Micrurus silviae 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Micrurus surinamensis 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Micrurus tricolor 1 0 0 1 0 0 1 1 0 0 0 0 0 1 0 0 0 Mussurana bicolor 1 0 0 1 0 0 1 1 1 0 0 0 0 1 1 1 1 Mussurana quimi 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Oxybelis aeneus 1 1 0 1 1 0 0 0 0 0 1 0 0 1 0 0 0 Oxybelis fulgidus 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Oxyrhopus guibei 1 1 0 1 1 0 1 1 0 0 0 1 0 0 0 0 0 Oxyrhopus melanogenys 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Oxyrhopus petolarius 1 1 0 1 0 0 1 0 0 0 1 0 0 1 0 0 1 Oxyrhopus rhombifer 1 1 1 1 1 1 1 0 0 0 1 0 1 1 1 1 1
161 PTN 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 Oxyrhopus trigeminus 1 1 1 1 1 1 1 0 0 1 0 0 0 1 1 0 0 Paraphimophis rustica 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Phalotris matogrossensis 1 0 1 1 0 1 1 1 0 0 1 0 0 1 1 0 0 Phalotris mertensi 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 Phalotris nasutus 1 0 0 0 0 0 0 0 0 1 0 0 0 0 1 0 0 Phalotris nigrilatus 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Phalotris tricolor 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0 0 Philodryas aestiva 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 Philodryas agassizii 0 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 Philodryas baroni 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 Philodryas livida 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 Philodryas mattogrossensis 1 0 0 0 0 1 0 1 0 1 0 0 0 1 0 1 0 Philodryas nattereri 1 1 1 1 1 0 1 0 0 1 1 0 0 0 0 0 0 Philodryas olfersii 1 1 1 1 1 0 1 0 1 1 1 1 1 1 1 0 1 Philodryas patagoniensis 1 1 0 1 1 0 1 0 0 1 1 0 0 1 0 0 1 Philodryas psammophidea 1 1 0 1 1 0 0 0 0 0 0 0 1 0 0 0 0 Philodryas varia 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Philodryas viridissima 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 Phimophis guerini 1 1 0 1 1 0 0 0 0 0 0 0 0 1 0 0 0 Phimophis vittatus 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Phrynonax poecilonotus 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Pseudoboa coronata 1 0 0 0 0 0 0 0 0 0 1 0 0 1 0 0 0 Pseudoboa nigra 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 0 0 Pseudoeryx plicatilis 1 0 1 1 0 0 1 1 1 0 1 0 0 1 0 0 1 Psomophis genimaculatus 1 0 0 1 0 0 1 1 1 0 0 0 0 1 1 0 1 Psomophis obtusus 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
162 PTN 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 Rena unguirostris 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Rhachidelus brazili 0 0 0 1 0 0 0 0 0 1 0 0 0 0 0 0 0 Sibynomorphus lavillai 1 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0 1 Sibynomorphus mikanii 1 1 1 1 1 0 1 0 0 1 0 0 0 1 1 0 1 Sibynomorphus turgidus 1 0 1 1 1 1 1 0 0 1 1 1 1 1 1 0 1 Sibynomorphus ventrimaculatus 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Simophis rhinostoma 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Siphlophis compressus 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Spilotes pullatus 1 1 1 1 1 1 1 0 0 1 1 1 1 1 0 0 1 Spilotes sulphureus 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 Tachymenis peruviana 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Taeniophallus occipitalis 1 1 1 1 1 0 1 0 1 1 1 1 1 1 1 0 0 Tantilla melanocephala 1 1 1 1 1 0 0 1 0 1 1 0 0 1 1 0 0 Thamnodynastes chaquensis 1 0 1 1 0 0 1 1 0 0 1 0 0 1 1 0 1 Thamnodynastes hypoconia 1 1 1 1 0 0 1 0 0 0 0 0 0 1 1 0 0 Thamnodynastes lanei 1 0 0 0 0 1 1 0 1 0 1 0 0 1 0 0 1 Thamnodynastes rutilus 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 Thamnodynastes strigatus 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Trilepida brasiliensis 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Trilepida koppesi 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Xenodon dorbignyi 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Xenodon histricus 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Xenodon matogrossensis 1 0 0 1 0 1 0 0 0 0 0 0 0 1 1 0 1 Xenodon merremii 0 1 0 1 1 1 1 0 0 1 1 1 1 1 0 1 1 Xenodon nattereri 1 0 0 1 0 0 0 0 0 1 0 0 0 0 0 0 0 Xenodon pulcher 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0 1 0
163 PTN 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 Xenodon rhabdocephalus 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 Xenodon severus 0 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 Xenopholis undulatus 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Xenopholis werdingorum 1 1 0 1 1 1 1 0 1 0 1 0 0 1 0 0 0
17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 Long -57.0 -56.0 -60.0 -58.0 -57.0 -56.5 -56.0 -55.5 -55.0 -58.0 -56.5 -60.0 -56.0 -56.5 -57.5 Lat -19.6 -19.6 -20.1 -20.1 -20.1 -20.1 -20.6 -20.6 -20.6 -21.6 -21.6 -22.6 -22.6 -24.6 -25.1 Amerotyphlops brongersmianus 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Amerotyphlops reticulatus 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Anilius scytale 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Apostolepis ambiniger 0 0 0 0 0 1 0 0 0 0 0 0 0 0 1 Apostolepis assimilis 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Apostolepis dimidiata 0 1 0 0 0 1 1 1 0 0 1 0 1 1 1 Apostolepis intermedia 0 0 0 0 0 1 0 1 0 0 0 0 0 0 0 Apostolepis nigroterminata 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Apostolepis vittata 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Atractus albuquerquei 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Atractus paraguayensis 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 Atractus reticulatus 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Atractus thalesdelemai 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Boa constrictor 0 1 1 0 1 1 1 1 1 0 1 1 0 1 1 Boiruna maculata 0 0 1 0 0 1 0 0 0 0 0 1 0 0 1 Bothrops alternatus 0 1 0 0 0 0 1 0 1 0 1 0 0 1 1 Bothrops diporus 0 0 1 0 0 0 0 0 0 0 0 1 0 0 1 Bothrops jararaca 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
164 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 Bothrops jararacussu 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Bothrops mattogrossensis 1 1 1 1 1 1 1 0 0 1 1 1 1 0 0 Bothrops moojeni 1 1 0 1 0 1 1 1 1 1 0 0 1 0 0 Bothrops pauloensis 1 1 0 0 0 0 1 0 1 0 1 0 1 0 0 Chironius bicarinatus 0 1 0 0 0 0 1 0 0 0 0 0 0 0 0 Chironius exoletus 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 Chironius flavolineatus 0 1 0 0 0 1 1 1 0 0 1 0 1 0 0 Chironius fuscus 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Chironius laurenti 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Chironius quadricarinatus 1 1 1 0 0 1 0 1 0 0 1 1 1 0 1 Chironius scurrulus 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Clelia clelia 1 1 0 0 0 0 1 0 0 0 0 0 0 0 1 Clelia langeri 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Clelia plumbea 1 1 0 0 0 1 0 0 0 0 0 0 0 0 1 Corallus hortulanus 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Crotalus durissus 1 1 1 0 1 1 1 1 1 1 1 0 1 1 1 Dipsas catesbyi 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Dipsas indica 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Drepanoides anomalus 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Drymarchon corais 0 1 1 1 1 1 1 0 1 1 1 0 1 1 0 Drymoluber brazili 0 0 0 0 0 1 0 1 1 0 0 0 0 0 0 Epicrates alvarezi 0 0 1 0 0 0 0 0 0 0 0 1 0 0 0 Epicrates cenchria 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Epicrates crassus 0 1 0 0 1 1 0 0 1 0 1 0 0 1 0 Epictia albipuncta 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Epictia munoai 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
165 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 Epictia vellardi 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Erythrolamprus aesculapii 0 1 0 0 0 1 1 0 0 0 1 0 1 1 1 Erythrolamprus albertguentheri 0 0 1 0 0 0 0 0 0 0 0 1 0 0 0 Erythrolamprus almadensis 0 1 0 0 0 1 1 1 0 0 1 0 0 1 0 Erythrolamprus frenatus 0 0 0 0 0 0 0 0 1 0 0 0 0 1 0 Erythrolamprus jaegeri 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 Erythrolamprus maryellenae 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Erythrolamprus miliaris 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Erythrolamprus poecilogyrus 1 1 1 1 1 1 1 1 0 1 1 1 1 1 1 Erythrolamprus reginae 1 0 0 0 1 1 0 0 0 0 0 0 0 1 0 Erythrolamprus sagittifer 0 0 1 0 0 0 0 0 0 0 0 1 0 0 0 Erythrolamprus semiaureus 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 Erythrolamprus taeniogaster 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Erythrolamprus typhlus 1 1 0 0 0 1 1 0 0 1 1 0 0 0 0 Eunectes murinus 1 1 0 1 0 1 1 1 0 0 1 0 1 0 0 Eunectes notaeus 1 0 0 1 1 1 1 0 0 0 0 0 0 1 1 Helicops angulatus 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Helicops infrataeniatus 0 0 0 0 0 0 0 0 1 0 0 0 1 0 0 Helicops leopardinus 1 1 0 1 0 1 1 1 0 1 0 0 0 1 1 Helicops modestus 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 Helicops polylepis 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Hydrodynastes bicinctus 0 0 0 0 0 1 1 0 0 0 0 0 0 0 0 Hydrodynastes gigas 1 1 0 1 0 1 1 0 0 1 0 0 0 0 1 Hydrops caesurus 0 1 0 0 0 1 0 0 0 0 1 0 0 0 0 Imantodes cenchoa 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Leptodeira annulata 1 1 1 0 1 1 1 0 0 1 1 1 0 0 1
166 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 Leptophis ahaetulla 1 1 0 1 1 1 1 1 0 1 1 0 1 1 1 Liotyphlops beui 0 0 0 0 0 0 0 1 1 0 0 0 1 0 0 Liotyphlops ternetzii 0 0 0 0 0 0 0 0 1 0 0 0 1 1 0 Lygophis dilepis 1 0 0 1 1 1 0 0 0 1 1 1 0 0 1 Lygophis flavifrenatus 0 0 0 0 1 1 0 0 0 0 0 0 1 1 0 Lygophis meridionalis 0 1 0 0 0 1 1 1 1 0 1 0 1 1 1 Lygophis paucidens 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Mastigodryas bifossatus 0 1 0 0 1 1 1 1 1 0 1 1 0 1 1 Mastigodryas boddaerti 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Micrurus altirostris 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Micrurus annellatus 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Micrurus baliocoryphus 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 Micrurus corallinus 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Micrurus diana 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Micrurus frontalis 0 0 0 0 1 1 1 0 1 0 1 0 1 1 1 Micrurus lemniscatus 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 Micrurus paraensis 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Micrurus pyrrhocryptus 1 0 0 1 0 0 0 0 0 0 0 0 0 0 0 Micrurus silviae 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Micrurus surinamensis 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Micrurus tricolor 1 0 0 0 1 1 1 0 0 1 0 0 0 0 0 Mussurana bicolor 1 1 0 1 1 1 1 1 0 0 0 0 0 0 1 Mussurana quimi 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Oxybelis aeneus 0 1 0 0 1 1 1 0 0 0 0 0 0 0 0 Oxybelis fulgidus 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Oxyrhopus guibei 0 1 0 0 0 1 1 1 1 0 1 0 1 1 1
167 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 Oxyrhopus melanogenys 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Oxyrhopus petolarius 0 1 0 1 1 1 1 0 0 0 0 0 0 0 0 Oxyrhopus rhombifer 1 1 1 1 0 1 1 0 0 1 0 1 0 0 0 Oxyrhopus trigeminus 0 1 0 0 1 1 1 1 0 0 0 0 0 0 0 Paraphimophis rustica 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Phalotris matogrossensis 0 1 0 0 0 1 1 1 0 0 1 0 0 0 1 Phalotris mertensi 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Phalotris nasutus 0 1 0 0 0 0 0 1 0 0 0 0 0 0 0 Phalotris nigrilatus 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 Phalotris tricolor 0 0 0 0 0 0 0 0 0 0 0 1 0 0 1 Philodryas aestiva 0 0 0 0 0 1 1 0 1 0 0 0 0 1 0 Philodryas agassizii 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 Philodryas baroni 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 Philodryas livida 0 0 0 0 1 1 1 0 0 0 0 0 0 0 0 Philodryas mattogrossensis 1 1 1 0 1 1 1 1 1 1 1 1 0 0 0 Philodryas nattereri 0 0 0 0 1 1 1 1 1 0 0 0 0 0 0 Philodryas olfersii 0 1 0 1 1 1 1 1 1 0 1 1 1 1 1 Philodryas patagoniensis 1 1 0 1 0 0 1 1 0 1 0 0 1 1 1 Philodryas psammophidea 0 1 1 0 1 0 1 0 0 0 1 1 0 0 0 Philodryas varia 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Philodryas viridissima 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Phimophis guerini 0 1 0 0 1 1 1 1 1 1 0 0 0 0 1 Phimophis vittatus 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 Phrynonax poecilonotus 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Pseudoboa coronata 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Pseudoboa nigra 1 1 0 0 1 1 1 1 1 1 1 0 1 0 0
168 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 Pseud oeryx plicatilis 1 1 0 1 0 1 1 0 0 0 0 0 0 0 0 Psomophis genimaculatus 1 1 1 0 0 1 1 0 0 1 0 1 0 0 0 Psomophis obtusus 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Rena unguirostris 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Rhachidelus brazili 0 0 0 0 1 1 0 0 0 0 0 0 0 1 0 Sibynomorphus lavillai 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Sibynomorphus mikanii 1 1 0 0 1 0 0 0 1 1 1 0 0 0 0 Sibynomorphus turgidus 0 1 1 1 1 1 1 1 0 1 1 1 0 1 1 Sibynomorphus ventrimaculatus 1 1 0 0 1 1 0 1 1 0 0 0 1 0 1 Simophis rhinostoma 0 1 0 0 0 0 0 0 1 0 0 0 0 0 0 Siphlophis compressus 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Spilotes pullatus 0 1 0 0 0 0 0 0 0 0 1 0 1 1 0 Spilotes sulphureus 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Tachymenis peruviana 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Taeniophallus occipitalis 0 1 0 0 0 0 1 1 0 1 1 0 1 0 1 Tantilla melanocephala 1 1 0 0 0 1 1 1 0 0 0 0 0 1 1 Thamnodynastes chaquensis 1 1 0 1 0 1 1 0 0 1 0 0 0 0 1 Thamnodynastes hypoconia 0 1 0 1 0 1 1 0 0 1 0 0 0 0 1 Thamnodynastes lanei 1 1 0 0 0 1 0 0 0 0 0 0 0 1 0 Thamnodynastes rutilus 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Thamnodynastes strigatus 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Trilepida brasiliensis 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Trilepida koppesi 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Xenodon dorbignyi 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Xenodon histricus 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Xenodon matogrossensis 0 1 0 0 0 1 1 1 0 1 0 0 0 0 0
169 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 Xenodon merremii 0 1 0 0 1 1 1 1 1 1 1 1 1 1 1 Xenodon nattereri 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 Xenodon pulcher 0 0 1 0 0 0 0 0 0 0 0 1 0 0 0 Xenodon rhabdocephalus 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Xenodon severus 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Xenopholis undulatus 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 Xenopholis werdingorum 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
1057 Table 2. Morphological characterization of Caenophidia snakes from Paraguay River Basin. N: number of specimens analized; SVL: snout-vent length; TL: tail 1058 length; CM: circumference around mid-body; VS: ventral scale count; SS: subcaudal scale count; HH-E: head height at eye position; HH-N: head height at 1059 nostril position; HW-E: head width at eye position; HW-N: head width at eye and nostril position; HE: height of inferior border of the eye; HN: height of the 1060 nostril; DE: distance between eyes; DN: distance between nostrils. Measures are in milimeters. SP N SVL TL CM VS SS HH-E HH-N HW-E HW-N HE HN DE DN Apostolepis ambiniger 3 459.00 41.33 22.33 225 28 3.99 2.68 5.07 3.40 2.88 1.66 4.31 3.40 Apostolepis assimilis 7 447.14 42.57 21.29 242 31 4.13 2.33 5.35 2.84 2.59 1.68 4.01 2.63 Apostolepis dimidiata 7 374.57 39.29 19.00 228 30 3.53 2.11 4.48 2.69 2.15 1.36 3.70 2.56 Apostolepis intermedia 1 278.00 36.00 13.00 210 31 2.71 1.40 2.99 1.89 1.54 0.93 2.60 1.72 Apostolepis nigroterminata 7 257.00 30.57 14.00 204 30 2.67 1.60 3.39 1.90 1.41 0.92 2.73 1.75
170 SP N SVL TL CM VS SS HH-E HH-N HW-E HW-N HE HN DE DN Apostolepis vittata 4 303.00 30.50 13.00 239 32 2.43 1.58 3.09 2.03 1.34 0.87 2.69 1.81 Atractus albuquerquei 5 302.80 39.60 21.00 172 31 4.14 2.51 5.25 3.37 2.26 1.71 4.03 2.85 Atractus paraguayensis 4 288.25 39.75 25.00 149 27 4.77 2.78 5.39 2.67 2.53 1.94 4.27 2.41 Boiruna maculata 6 996.17 216.67 85.33 225 75 13.60 7.99 17.23 9.41 7.77 5.32 12.62 8.24 Bothrops alternatus 5 706.00 110.20 83.20 174 46 13.01 8.66 16.48 7.89 9.05 5.53 14.35 7.46 Bothrops diporus 7 690.14 108.14 84.43 175 48 14.27 8.38 16.44 7.20 8.91 5.92 14.77 6.73 Bothrops mattogrossensis 13 599.54 104.85 68.54 173 50 11.69 6.65 14.37 6.10 7.36 4.58 12.33 5.83 Bothrops moojeni 7 735.71 136.43 85.14 193 65 13.18 8.00 17.61 7.54 8.54 5.00 15.72 7.32 Bothrops pauloensis 7 525.00 89.86 50.57 170 48 9.87 6.03 12.49 5.42 5.76 3.96 10.82 5.21 Chironius bicarinatus 5 944.80 536.00 68.40 159 136 12.03 6.87 14.19 8.56 4.80 4.46 13.33 8.19 Chironius exoletus 7 750.86 440.86 55.00 153 131 10.97 6.27 13.07 7.65 4.46 3.86 10.76 6.95 Chironius flavolineatus 7 577.86 385.57 35.29 154 137 7.47 5.13 8.99 5.12 3.08 2.56 7.90 4.93 Chironius laurenti 6 1129.50 590.17 92.33 162 135 14.06 7.92 17.49 9.95 6.03 4.81 15.33 9.47 Chironius quadricarinatus 8 627.88 352.75 44.00 149 112 9.23 5.39 10.60 6.26 3.83 3.47 8.99 6.01 Chironius scurrulus 6 1085.83 543.00 85.50 155 116 14.98 8.14 16.46 9.58 6.49 5.40 15.09 9.04 Clelia clelia 4 971.75 250.00 96.50 211 73 12.50 7.17 15.34 8.24 7.09 4.68 11.56 7.21 Clelia plumbea 5 1099.40 276.40 87.20 221 83 13.55 7.98 16.97 9.47 7.81 5.40 12.76 8.57 Crotalus durissus 9 915.44 103.44 136.33 174 29 16.74 11.36 23.77 10.26 12.02 8.64 20.65 9.32 Dipsas indica 2 584.50 235.00 29.00 198 114 8.85 5.84 8.83 5.25 3.17 3.52 8.04 5.25
171 SP N SVL TL CM VS SS HH-E HH-N HW-E HW-N HE HN DE DN Drymarchon corais 6 1464.17 318.17 130.00 207 76 22.02 13.45 26.02 13.60 11.94 8.18 21.89 12.25 Drymoluber brazili 5 813.00 391.20 55.40 186 116 9.98 6.16 13.01 6.93 4.29 3.98 10.23 6.08 Erythrolamprus aesculapii 7 536.14 80.71 42.14 196 41 7.70 4.45 8.66 5.32 3.83 2.83 7.24 4.99 Erythrolamprus albertguentheri 6 459.67 89.67 41.50 187 51 7.66 4.66 9.42 5.22 3.95 3.28 6.66 4.58 Erythrolamprus almadensis 8 324.63 93.63 31.25 156 61 6.23 3.43 6.78 3.90 3.05 2.26 4.92 3.19 Erythrolamprus frenatus 4 457.75 83.75 37.00 191 50 6.86 4.51 7.69 4.86 3.41 3.28 6.02 4.04 Erythrolamprus jaegeri 8 310.50 96.25 29.63 157 66 5.37 3.11 6.01 3.36 2.33 2.09 4.67 2.84 Erythrolamprus maryellenae 4 290.25 89.50 27.00 153 64 5.45 3.07 6.70 3.47 2.66 2.29 4.45 2.92 Erythrolamprus miliaris 6 554.33 125.67 56.00 165 55 9.82 6.26 11.98 6.73 5.37 4.60 8.10 5.43 Erythrolamprus poecilogyrus 13 385.31 85.69 42.69 154 49 6.92 4.52 9.69 5.10 4.06 3.23 6.11 4.23 Erythrolamprus reginae 7 436.57 173.29 38.14 152 80 7.44 4.08 8.95 4.81 3.39 3.01 6.58 4.29 Erythrolamprus sagittifer 7 451.43 173.14 40.86 182 90 7.31 4.28 8.88 4.80 3.53 3.34 6.21 4.19 Erythrolamprus semiaureus 2 680.00 146.50 58.50 180 57 11.91 6.55 13.90 7.04 7.41 5.09 9.93 6.33 Erythrolamprus taeniogaster 5 368.80 90.00 41.20 151 54 7.02 4.04 8.30 5.02 3.80 2.94 6.30 4.36 Erythrolamprus typhlus 5 371.00 70.80 32.60 172 44 6.65 3.83 8.41 4.51 3.34 2.72 5.88 4.03 Helicops angulatus 7 316.00 193.86 39.43 111 87 6.70 4.17 8.76 4.92 3.68 3.71 5.47 2.62 Helicops infrataeniatus 6 332.67 147.50 42.83 129 71 7.92 5.04 8.71 4.68 4.54 4.48 5.49 2.43 Helicops leopardinus 14 318.50 143.00 42.86 116 69 7.11 4.31 8.93 5.03 4.26 3.90 5.33 2.43 Helicops modestus 7 296.86 133.14 41.29 120 75 6.29 3.91 7.53 4.07 3.60 3.54 4.38 2.00
172 SP N SVL TL CM VS SS HH-E HH-N HW-E HW-N HE HN DE DN Helicops polylepis 5 393.60 236.00 55.60 126 95 7.98 5.39 10.05 6.49 5.19 4.94 6.27 2.95 Hydrodynastes bicinctus 3 851.67 317.33 118.00 167 80 16.96 9.64 21.55 11.41 12.46 7.92 14.55 8.70 Hydrodynastes gigas 6 1197.00 389.50 137.83 161 73 19.86 11.87 24.55 12.54 12.90 9.44 17.54 10.35 Hydrops caesurus 4 338.75 90.75 29.50 147 54 4.90 2.63 6.59 3.72 2.77 2.33 4.29 1.77 Imantodes cenchoa 4 640.50 275.25 20.25 261 159 5.24 2.66 6.96 3.16 1.88 1.85 5.40 2.98 Leptodeira annulata 13 438.08 149.92 35.62 193 83 6.96 4.18 9.18 4.74 3.29 2.78 6.38 4.28 Leptophis ahaetulla 10 749.00 428.70 53.90 160 134 9.81 5.48 11.32 5.85 5.17 4.10 9.25 5.28 Lygophis dilepis 7 358.29 105.71 27.29 171 67 5.56 3.13 6.02 3.47 2.41 2.25 4.35 3.05 Lygophis flavifrenatus 6 350.33 136.17 27.83 161 81 5.98 3.37 6.39 3.34 2.45 2.48 4.78 2.92 Lygophis meridionalis 6 393.50 154.00 31.00 169 84 6.12 3.52 6.97 3.53 2.46 2.57 4.80 3.13 Lygophis paucidens 7 327.57 112.71 24.86 170 74 5.11 3.09 5.86 3.01 2.12 2.14 4.18 2.72 Mastigodryas bifossatus 9 1073.33 394.11 106.78 169 90 16.20 9.88 19.44 9.65 8.90 6.83 14.79 8.03 Mastigodryas boddaerti 4 758.00 286.25 50.00 186 107 10.32 6.12 11.27 5.91 4.49 3.86 10.67 5.67 Micrurus baliocoryphus 4 736.50 55.00 44.50 224 26 9.58 7.00 11.36 7.00 6.32 5.10 8.50 6.21 Micrurus diana 1 804.70 51.70 53.75 218 23 9.82 7.34 11.19 7.01 6.33 4.65 9.41 6.71 Micrurus frontalis 8 960.00 58.00 53.75 224 22 9.82 7.34 11.19 7.01 6.33 4.65 9.41 6.71 Micrurus lemniscatus 6 826.83 78.33 42.50 236 35 9.36 7.10 12.70 8.30 7.25 5.35 7.89 6.33 Micrurus pyrrhocryptus 6 660.83 47.33 40.00 234 28 7.92 5.82 9.92 6.22 4.93 4.17 7.23 5.47 Micrurus surinamensis 6 665.67 95.17 62.50 160 36 10.83 7.15 14.40 7.55 8.09 5.84 8.29 6.10
173 SP N SVL TL CM VS SS HH-E HH-N HW-E HW-N HE HN DE DN Micrurus tricolor 5 658.80 49.60 41.00 224 27 8.01 5.78 10.51 6.26 4.95 4.15 7.79 5.66 Mussurana bicolor 12 456.58 136.08 45.08 170 67 7.11 4.48 8.93 4.66 3.72 3.32 6.04 4.00 Oxybelis aeneus 7 646.29 420.29 22.00 188 164 6.42 2.86 5.61 3.00 2.55 2.18 5.26 2.78 Oxybelis fulgidus 7 1015.86 504.29 52.86 205 153 11.02 4.30 12.81 5.11 5.01 2.80 11.77 5.02 Oxyrhopus guibei 7 528.29 160.00 38.43 195 81 6.85 4.65 8.18 4.74 3.47 3.29 6.43 4.02 Oxyrhopus petolarius 7 640.57 212.86 42.71 211 99 7.25 4.74 8.78 4.84 3.25 3.31 6.50 4.41 Oxyrhopus rhombifer 9 406.56 111.67 32.89 198 78 5.33 3.38 6.77 3.63 2.35 2.40 4.87 3.12 Oxyrhopus trigeminus 6 499.17 143.50 35.67 201 82 6.52 3.93 8.23 4.44 2.92 2.62 5.93 3.73 Phalotris matogrossensis 6 405.67 42.33 27.50 203 30 5.15 3.49 6.80 3.55 3.48 2.44 4.43 3.24 Phalotris mertensi 4 587.25 56.75 28.00 228 33 6.43 4.18 7.69 4.14 4.21 2.80 5.97 3.92 Phalotris nasutus 4 380.25 58.50 25.50 182 37 3.96 2.09 6.20 3.15 2.60 1.30 4.47 3.01 Phalotris nigrilatus 1 380.25 58.50 25.50 182 37 3.96 2.09 6.20 3.15 2.60 1.30 4.47 3.01 Phalotris tricolor 1 516.00 56.00 30.00 206 30 5.89 3.58 7.88 3.92 4.20 2.32 4.83 3.48 Philodryas aestiva 4 550.50 251.50 39.25 192 119 7.51 4.78 8.16 4.48 3.20 2.94 6.78 4.11 Philodryas agassizii 3 300.67 105.00 26.67 129 63 5.96 3.13 6.24 3.30 2.87 2.20 4.85 3.13 Philodryas baroni 3 1105.67 462.00 90.00 230 131 12.38 7.81 14.48 7.09 5.67 5.19 12.21 6.50 Philodryas livida 1 614.00 170.00 57.13 154 83 10.56 6.13 11.60 5.69 5.21 4.19 8.85 5.35 Philodryas mattogrossensis 8 810.88 374.75 65.75 223 135 10.72 6.66 11.53 6.43 5.23 4.85 9.28 5.92 Philodryas nattereri 8 799.13 363.50 68.63 204 125 11.07 6.74 12.60 6.10 4.93 4.72 10.22 5.78
174 SP N SVL TL CM VS SS HH-E HH-N HW-E HW-N HE HN DE DN Philodryas olfersii 10 618.00 272.10 43.10 191 114 8.39 5.14 9.71 5.27 4.08 3.39 8.41 5.01 Philodryas patagoniensis 8 648.00 275.63 57.13 174 105 10.56 6.13 11.60 5.69 5.21 4.19 8.85 5.35 Philodryas psammophidea 3 553.33 205.33 41.67 193 96 7.63 4.71 8.58 4.43 3.67 3.31 7.29 4.05 Philodryas viridissima 4 749.50 298.25 58.00 220 123 10.94 6.68 13.01 6.49 6.05 4.82 10.51 5.92 Phimophis guerini 5 644.40 150.60 60.40 199 69 8.09 4.82 10.96 6.47 4.25 3.31 7.80 5.85 Phimophis vittatus 5 537.60 109.20 50.60 202 54 8.11 4.68 9.16 4.65 4.10 2.86 6.20 4.16 Phrynonax poecilonotus 5 811.80 313.00 63.60 195 118 13.34 8.62 14.76 7.99 5.51 5.63 14.00 7.91 Pseudoboa coronata 6 586.67 228.33 48.17 188 91 8.16 5.38 10.57 6.84 4.31 3.58 8.05 5.94 Pseudoboa nigra 6 787.33 278.33 77.00 207 98 9.67 5.86 11.84 7.03 5.08 3.78 8.91 6.04 Pseudoeryx plicatilis 5 571.40 137.80 80.00 131 44 9.65 5.62 9.55 5.50 5.51 4.88 6.71 2.40 Psomophis genimaculatus 8 281.88 79.00 20.38 192 62 3.91 2.37 4.89 2.58 1.80 1.61 3.58 2.24 Rhachidelus brazili 4 1108.50 252.00 123.50 187 64 14.26 9.48 18.14 9.81 7.78 6.69 13.53 8.52 Sibynomorphus lavillai 5 372.00 105.60 28.20 163 56 7.55 4.57 7.77 4.64 3.81 3.25 6.63 4.42 Sibynomorphus mikanii 5 337.00 83.20 26.00 165 49 5.66 3.85 6.65 4.02 2.81 2.56 4.88 3.46 Sibynomorphus turgidus 12 295.33 70.92 26.42 154 47 5.45 3.58 6.10 3.67 2.79 2.42 4.56 3.24 Sibynomorphus ventrimaculatus 6 308.33 85.67 29.83 161 54 6.38 3.91 7.20 4.03 3.46 2.62 5.63 3.94 Simophis rhinostoma 4 473.75 125.50 32.00 175 66 7.56 4.40 9.29 5.28 3.78 2.76 7.16 4.77 Siphlophis compressus 6 580.33 192.17 30.50 240 107 6.35 4.04 7.81 4.54 2.34 2.80 6.30 3.77 Spilotes pullatus 6 1293.33 447.33 104.50 210 115 16.25 9.92 19.04 11.64 8.54 6.23 17.28 9.80
175 SP N SVL TL CM VS SS HH-E HH-N HW-E HW-N HE HN DE DN Spilotes sulphureus 5 1299.60 522.20 92.00 212 136 18.15 11.04 20.59 12.16 9.13 7.02 19.43 11.54 Taeniophallus occipitalis 7 315.29 116.57 20.29 176 78 4.22 2.38 5.14 2.72 1.51 1.40 4.42 2.46 Tantilla melanocephala 8 217.38 64.88 15.75 152 55 2.91 1.85 3.88 2.46 1.26 1.19 3.37 2.23 Thamnodynastes chaquensis 13 438.85 128.77 42.00 149 64 8.36 4.51 9.37 4.45 3.90 2.96 7.42 3.99 Thamnodynastes hypoconia 9 353.22 137.00 30.11 144 74 6.46 3.69 7.18 3.42 2.93 2.53 5.59 2.97 Thamnodynastes lanei 3 352.67 133.33 23.00 151 76 5.56 3.20 6.07 3.03 2.55 2.24 4.95 2.60 Thamnodynastes rutilus 4 330.50 146.00 39.50 125 74 7.22 4.13 7.86 4.06 4.11 3.37 6.13 3.30 Xenodon matogrossensis 5 351.00 58.20 47.20 135 32 8.59 4.67 10.11 5.60 5.02 2.90 6.63 4.94 Xenodon merremii 16 582.19 108.56 66.69 141 41 12.47 7.29 15.27 8.43 7.12 4.66 8.90 6.50 Xenodon nattereri 1 358.00 74.60 48.20 135 39 8.28 4.94 9.96 5.47 5.66 2.95 6.31 4.35 Xenodon pulcher 8 390.88 63.50 51.88 158 35 8.06 4.44 10.07 5.65 4.92 2.69 6.65 4.75 Xenodon rhabdocephalus 5 513.60 94.80 51.60 139 45 11.10 6.31 12.79 6.86 5.75 4.59 9.68 6.06 Xenodon severus 4 576.75 94.00 71.75 135 38 12.98 7.34 17.47 9.74 6.48 5.17 13.62 8.44 Xenopholis undulatus 1 335.67 60.17 24.50 176 39 4.90 2.82 7.05 3.77 2.99 2.02 4.69 3.16 Xenopholis werdingorum 6 335.67 60.17 24.50 174 39 4.90 2.82 7.05 3.77 2.99 2.02 4.69 3.16
176 1061 Especimens consulted to gathering morphological measures. 1062 Museums acronyms ar as follow: CZCEN, Colección Zoológica de la Facultad de Ciencias Exactas y Naturales; FML, Fundación Miguel Lillo; IB, Instituto 1063 Butantã; LPR, Colección de Reptiles del Museo de La Plata; MACN, Museo Argentino de Ciencias Naturales; MNHNP, Museo Nacional de Historia Natural del 1064 Paraguay; MUZUSPS, Coleção de Serpentes do Museu de Zoologia d Universidade de São Paulo; UFMTR, Coleção de Répteis da Universidade Federal de 1065 Mato Grosso; UNNEC, Colección Herpetológica de la Universidad Nacional del Nordeste; ZUFMS, Coleção Zoológica de Referência da Universidade Federal 1066 de Mato Grosso do Sul 1067 1068 Apostolepis ambiniger: IB10005, MNHNP3493, MNHNP5163; Apostolepis assimilis: ZUFMS-CEUCH05081, ZUFMS-REP00233, IB087318, IB022405, IB00816, 1069 IB009466 ,IB009115; Apostolepis dimidiata: MNHNP1180, UNNEC12232, MZUSPS15294, MZUSPS15292, IB04817, IB081522, IB082510; Apostolepis 1070 intermedia: ZUFMS-REP1269; Apostolepis nigroterminata: UFMTR05502, UFMTR10672, UFMTR11293, UFMTR01178, UFMTR01215, UFMTR01118, 1071 UFMTR01172; Apostolepis vittata: UFMTR-COM163, ZUFMS-NH569, ZUFMS-NH496, MZUSP111461; Atractus albuquerquei: UFMTR09368, UFMTR09356, 1072 UFMTR00325, UFMTR09357, UFMTR09378; Atractus paraguayensisi: FML06221, IB30196, IB50857, IB50864; Boiruna maculata: MNHNP6553, 1073 MNHNP2619, MNHNP7674, MNHNP2625, MNHNP7937, UFMTR02376; Bothrops alternatus: MNHNP11039, UNNEC10138, UNNEC6968, UNNEC10136, 1074 FML11410; Bothrops diporus: MNHNP9050, MNHNP2588, MNHNP9735, CZCEN0197, LPR5207, MACN37367, UNNEC8026; Bothrops mattogrossensis: 1075 MNHNP10189, MNHNP8432, MNHNP4012, MNHNP10453, CZCEN0296, ZUFMS-NH358-4714, ZUFMS-NH349-4713, ZUFMS-CEUCH5757, ZUFMS-VLF125, 1076 ZUFMS-CEUCH4718, FML02612, UFMTR11222, UFMTR01896; Bothrops moojeni: MNHNP3910, MNHNP8394, MNHNP8474, MNHNP6831, UFMTR01950, 1077 ZUFMS-REP01365, ZUFMS-REP01871; Bothrops pauloensis: MNHNP2595, UFMTR00382, UFMTR00512, UFMTR01688, UFMTR01691, ZUFMS-REP01871, 1078 ZUFMS-REP01356; Chironius bicarinatus: MACN791, FML06715, MZUSPS19586, MZUSPS18039, MZUSP11578; Chironius exoletus: ZUFMS-CEUCH2192, 1079 ZUFMS-CEUCH2189, ZUFMS-CEUCH2187, ZUFMS-CEUCH2188, ZUFMS-CEUCH0962, UFMTR11540, UFMTR00571; Chironius flavolineatus: MNHNP5201, 1080 ZUFMS-CEUCH3523, ZUFMS-CEUCH4976, ZUFMS-CEUCH3031, UFMTR06785, UFMTR00548, UFMTR00547; Chironius laurenti: ZUFMS-CEUCH2164,
177 1081 UFMTR03538, UFMTR00934, UFMTR01559, UFMTR07443; Chironius quadricarinatus: CZCEN512, FML26130, ZUFMS-REP1581, ZUFMS-REP2147, FML06174, 1082 UFMTR0055, UFMTR00565, UFMTR01525; Chironius scurrulus: UFMTR05716, UFMTR08553, UFMTR01540, UFMTR07291, UFMTR05363, UFMTR10604; 1083 Clelia clelia: MNHNP3085, MNHNP8489, MACN36843, UNNEC8034; Clelia plumbea: FML01830, UFMTR01915, UFMTR02813, UFMTR02363, MZUSPS19939; 1084 Crotalus durissus: ZUFMS-REP1890-2049, ZUFMS-REP1877-136, ZUFMS-REP1877-125, ZUFMS-REP1883, ZUFMS-REP1894, ZUFMS-REP1876, MNHNP3068, 1085 MNHNP3073, MACN44589; Dipsas indica: MZUSPS18607, MZUSPS18606; Drymarchon corais: MNHNP6531, MNHNP1814, MNHNP8366, MNHNP7943, 1086 UFMTR06674, UFMTR06764; Drymoluber brazili: UFMTR06970, MZUSPS20556, IB26716, IB25379, IB10465; Erythrolamprus_aesculapii: MNHNP3428, 1087 MNHNP2688, UFMTR06180, UFMTR02612, UFMTR00482, ZUFMS-REP01385, ZUFMS-REP01595; Erythrolamprus albertguentheri: MNHNP6592, 1088 MNHNP2631, MNHNP3783, UNNEC9422, FML00422, FML02627; Erythrolamprus almadensis: MNHNP5204, MACN45005, UNNEC210, FML11073, 1089 FML14893, UFMTR02191, UFMTR01961, UFMTR02079; Erythrolamprus frenatus: LPR-JW1017, UNNEC478,ZUFMS-VLF00153, IB78129; Erythrolamprus 1090 jaegeri: MNHNP10652, UNNEC351, UNNEC10878, UNNEC5403, FML11316, FML11309, FML11261, ZUFMS-CEUCH04984; Erythrolamprus maryellenae: 1091 MZUSP09927, IB01209, IB12559, IB63142; Erythrolamprus miliaris: MNHNP3777, MACN3198, MACN3174, UNNEC10666, FML14889, UFMTR-CSMT2400; 1092 Erythrolamprus poecilogyrus: ZUFMS-REP1283, ZUFMS-REP1006-CH285, ZUFMS-REP1278, ZUFMS-REP01685-00422, ZUFMS-REP1125, ZUFMS-REP1550, 1093 UNNEC706, MNHNP10925, MNHNP2544, MNHNP5126, MNHNP4201, MNHNP6696, MNHNP2637; Erythrolamprus reginae: MNHNP5192, FML23548, 1094 UFMTR11155, UFMTR03013, UFMTR03710, UFMTR10204, UFMTR09014; Erythrolamprus sagittifer: MNHNP7230, MNHNP5194, MNHNP4214, 1095 MNHNP7936, MNHNP5197, MNHNP3491, MACN41977; Erythrolamprus semiaureus: MNHNP6574, MNHNP2527; Erythrolamprus taeniogaster: 1096 UFMTR08026, UFMTR03684, UFMTR08028, UFMTR10718, MZUSPS17251; Erythrolamprus typhlus: ZUFMS-REP 1130, ZUFMS-CEUCH4406, ZUFMS-VLF124, 1097 FML00536, UFMTR01680; Helicops angulatus: UFMTR00518, UFMTR07393, UFMTR06807, UFMTR06806, UFMTR06835, UFMTR02181, UFMTR07440; 1098 Helicops infrataeniatus: MNHNP2632, MNHNP5203, MACN35080, UNNEC317, UNNEC323, UFMTR04351; Helicops leopardinus: MNHNP5048, MNHNP8524, 1099 MNHNP5124, MNHNP5557, MNHNP9199, MNHNP7309, ZUFMS-REP1327, ZUFMS-CEUCH097, ZUFMS-CEUCH660, ZUFMS-CEUCH662, ZUFMS-CEUCH664, 1100 ZUFMS-CEUCH646, UFMTR08290, UFMTR00014; Helicops modestus: ZUFMS-REP1538, ZUFMS-REP1584, MZUSPS17745, MZUSPS17740, MZUSP02789,
178 1101 MZUSP03266, MZUSP03279; Helicops polylepis: MZUSP05120, MZUSP09124, MZUSP04781, MZUSP10594, MZUSP08637; Hydrodynastes bicinctus: 1102 UFMTR08037, UFMTR09593, MZUSP08019; Hydrodynastes gigas: MNHNP5045, MNHNP5047, MNHNP7940, MNHNP8419, MNHNP10690, UNNEC9598; 1103 Hydrops caesurus: FML24887, FML24888, UFMTR01192, UFMTR08684; Imantodes cenchoa: MNHNP7673, FE202, IB87713, IB86124; Leptodeira annulata: 1104 MNHNP3595, MNHNP3776, MNHNP3396, MNHNP2513, ZUFMS-VLF0283-NHU03, ZUFMS-VLF0264-N88, ZUFMS-NH718-4334, ZUFMS-REP1292, ZUFMS- 1105 CEUCH3513, ZUFMS-RN194, ZUFMS-VLF0083, ZUFMS-REP1531-00166, ZUFMS-REP2177, PRF77; Leptophis ahaetulla: MNHNP6676, MNHNP2525, 1106 MNHNP0331, MNHNP2521, MNHNP2518, MNHNP2520, ZUFMS-REP1576, ZUFMS-REP1917, UNNEC499, UFMTR11775; Lygophis dilepis: MNHNP5053, 1107 MNHNP5476, MNHNP3123, MNHNP5248, UNNEC9710, UNNEC6598, FML11924; Lygophis flavifrenatus: MACN47377, UNNEC11263, UNNEC11013, 1108 UNNEC10226, FML16253, UFMTR03624; Lygophis meridionalis: MNHNP2673, UNNEC10819, UNNEC9333, UFMTR00659, UFMTR00299, ZUFMS-VLF00273; 1109 Lygophis paucidens: UFMTR00588, UFMTR11313, MZUSPS18697, MZUSP10797, MZUSP11560, MZUSPS14969, MZUSPS14970; Mastigodryas bifossatus: 1110 MNHNP9439, MNHNP10728, MNHNP10078, LPR-JW1510, UNNEC75, FML11078, FML11129, UFMTR08417, ZUFMS-REP02011; Mastigodryas boddaerti: 1111 UFMTR01637, UFMTR01408, UFMTR07217, UFMTR04878; Micrurus_baliocoryphus: MNHNP5044, UNNEC279, UNNEC376, UNNEC121; Micrurus frontalis: 1112 MNHNP2685, MNHNP2687, MNHNP2600, MNHNP5139, MNHNP3405, ZUFMS-REP02016, ZUFMS-REP1416, ZUFMS-REP1836; Micrurus lemniscatus: 1113 MNHNP5145, ZUFMS-VLF00180, MZUSPS19708, MZUSPS15534, MZUSP04792, MZUSPS17325; Micrurus pyrrhocryptus: MNHNP4018, MNHNP9227, 1114 MNHNP2686, CZCEN983, FML11446, UFMTR01197; Micrurus surinamensis: UFMTR07798, UFMTR07799, UFMTR07170, UFMTR07210, MZUSP08716, 1115 MZUSPS20838; Micrurus tricolor: UFMTR02182, UFMTR11712, UFMTR11782, UFMTR11786, UFMTR11785; Mussurana bicolor: MNHNP3942, 1116 MNHNP11188, MNHNP7684, MNHNP2616, MNHNP6552, MNHNP6674, MNHNP9226, ZUFMS-CEUCH3719, ZUFMS-CEUCH3720, ZUFMS-CEUCH5100, 1117 ZUFMS-CEUCH5175, UFMTR11219; Oxybelis aeneus: LPR-JW0694, UFMTR01717, UFMTR00611, UFMTR01427, UFMTR06650, UFMTR05954, ZUFMS- 1118 CEUCH00850; Oxybelis fulgidus: UFMTR010375, UFMTR08576, UFMTR09817, UFMTR010582, MZUSPS20450, MZUSP11417, MZUSPS19281; Oxyrhopus 1119 guibei: MNHNP3495, MACN44794, MACN37125, FML11328, UFMTR02194, UFMTR07657, ZUFMS-REP00244; Oxyrhopus petolarius: UFMTR3795, 1120 UFMTR08239, UFMTR06035, UFMTR05972, UFMTR09217, ZUFMS-REP01420, ZUFMS-CEUCH00196; Oxyrhopus rhombifer: MNHNP3496, MNHNP5149,
179 1121 MNHNP5151, MNHNP10035, MNHNP2569, MNHNP4056, CHEUCH 4669, UFMTR09877, UFMTR11436; Oxyrhopus trigeminus: UFMTR09865, UFMTR00825, 1122 UFMTR00824, UFMTR02193, UFMTR02125, UFMTR08283; Phalotris matogrossensis: MNHNP2627, UFMTR04124, UFMTR011791, UFMTR10494, ZUFMS- 1123 REP00186, ZUFMS-REP01426; Phalotris mertensi: IB00110, IB02631, IB15828, IB08570; Phalotris nasutus: UFMTR03773, UFMTR01471, ZUFMS- 1124 CEUCH03549, ZUFMS-CEUCH03696; Phalotris tricolor: MNHNP10574; Philodryas aestiva: UNNEC9905, UNNEC4677, FML23923, FML25059; Philodryas 1125 agassizii: UFMTR11118, UFMTR11122, UFMTR09364; Philodryas baroni: MNHNP2571, FML02542, FML02008; Philodryas mattogrossensis: MNHNP10658, 1126 MNHNP2653, MNHNP2574, MNHNP2573, MNHNP6525, MACN37365, UFMTR11081, UFMTR02162; Philodryas nattereri: UFMTR02602, UFMTR00098, 1127 UFMTR02322, UFMTR02158, UFMTR00283, UFMTR01244, UFMTR00497, ZUFMS-REP2030; Philodryas olfersii: MNHNP8851, MNHNP5179, MNHNP11128, 1128 ZUFMS-REP 1525, ZUFMS-REP1713, ZUFMS-REP1431, ZUFMS-REP00986, ZUFMS-VLF 119, UFMTR01908, UFMTR02600; Philodryas patagoniensis: ZUFMS- 1129 REP0276, ZUFMS-CEUCH3777, ZUFMS-CEUCH1865, MNHNP8523, MNHNP6675, MNHNP11186, MNHNP5227, UFMTR09967; Philodryas psammophidea: 1130 MACN38656, UNNEC722, UNNEC186; Philodryas viridissima: UFMTR01906, UFMTR11707, MZUSP019786, MZUSP11250; Phimophis guerini: MACN37098, 1131 MZUSPS14569, MZUSPS14468, MZUSPS14392, MZUSPS12699; Phimophis vittatus: MNHNP2577, MNHNP5164, MNHNP3498, MNHNP10657, UNNEC10618; 1132 Pseudoboa coronata: UFMTR01246, UFMTR03714, UFMTR03732, UFMTR03728, UFMTR03726, UFMTR03719; Philodryas nigra: ZUFMS-REP2044, ZUFMS- 1133 REP1510, MNHNP0309, MNHNP3594, MNHNP7548, UFMTR4251; Pseudoeryx plicatilis: ZUFMS-REP2047, MNHNP8788, CZCEN0683, ZUFMS-CEUCH0094, 1134 UFMTR01769; Pseutes poecilonotus: UFMTR03905,, UFMTR08844, UFMTR08845, UFMTR03939, UFMTR08048; Pseustes sulphureus: UFMTR05251, 1135 UFMTR08569, UFMTR8225, MZUSP07652, MZUSP09094; Psomophis genimaculatus: ZUFMS-CEUCH4400, ZUFMS-CEUCH4402, ZUFMS-CEUCH4684, ZUFMS- 1136 REP1443, ZUFMS-REP1441, MNHNP4199, MNHNP4200, UFMTR11294; Rhachidelus brazili: MACN1256, UFMTR01913, UFMTR05465, MZUSPS13341; 1137 Sibynomorphus lavillai: ZUFMS-CEUCH1008, ZUFMS-CEUCH1676, ZUFMS-CEUCH3551, ZUFMS-CEUCH1171, MACN33501; Sibynomorphus mikanii: ZUFMS- 1138 REP2052, UFMTR11303,UFMTR11874, MZUSPS14604, MZUSPS16905; Sibynomorphus turgidus: ZUFMS-REP2116, ZUFMS-REP2115, ZUFMS-CEUCH0926, 1139 ZUFMS-CEUCH1368, ZUFMS-CEUCH0331, ZUFMS-VLF0266, MNHNP10915, MNHNP10649, MNHNP3393, MNHNP10017, MNHNP3346, MNHNP2682; 1140 Sibynomorphus ventrimaculatus: ZUFMS-REP2089, ZUFMS-REP2097, MNHNP3774, UNNEC9966, FML06716, FML02384; Simophis rhinostoma: ZUFMS-
180 1141 REP1649, UFMTR00244, MZUSPS14745, MZUSPS21192; Siphlophis compressus: UFMTR08186, UFMTR09124, UFMTR03670, UFMTR03671, UFMTR03674, 1142 UFMTR03666; Spilotes pullatus: ZUFMS-CEUCH2120, ZUFMS-CEUCH1174, ZUFMS-CEUCH2119, MNHNP3054, MNHNP3057, MNHNP3053; Taeniophallus 1143 occipitalis: ZUFMS-CEUCH1038, ZUFMS-CEUCH2994, ZUFMS-CEUCH3753, ZUFMS-CEUCH4674, MACN38575, UFMTR09370, UFMTR09362; Tantilla 1144 melanocephala: ZUFMS-CEUCH1984, ZUFMS-CEUCH1976, ZUFMS-CEUCH5329, ZUFMS-CEUCH4965, ZUFMS-CEUCH4409, MNHNP10627, UFMTR00416, 1145 UFMTR00568; Thamnodynastes chaquensis: ZUFMS-CEUCH5321, ZUFMS-CEUCH5745, ZUFMS-CEUCH3800, ZUFMS-REP1205, ZUFMS-REP0970, ZUFMS- 1146 REP1621, ZUFMS-REP1015, MNHNP8483, MNHNP7387, MNHNP3353, MNHNP7388, MNHNP11051, MACN36729; Thamnodynastes hypoconia: ZUFMS- 1147 REP1042, MNHNP6569, MNHNP7563, MNHNP7589, MNHNP7586, MNHNP6567, MACN46534, FML11293, UFMTR00347; Thamnodynastes lanei: ZUFMS- 1148 CEUCH0199, UNNEC-CHINM1918, MZUSPS20692; Thamnodynastes rutilus: MZUSPS17584, MZUSPS17557, MZUSPS17555, MZUSPS17561; Xenodon 1149 matogrossensis: ZUFMS-REP1471, ZUFMS-REP1523, ZUFMS-REP1648, ZUFMS-REP1700, ZUFMS-REP2001; Xenodon merremii: ZUFMS-REP1487, ZUFMS- 1150 REP1495, ZUFMS-REP1493, ZUFMS-REP1723, ZUFMS-REP1478, ZUFMS-CEUCH001, ZUFMS-CEUCH0090, ZUFMS-CEUCH1175, MNHNP2659, MNHNP2663, 1151 MNHNP2664, MNHNP2610, MNHNP2658, MNHNP9992, FML11230, UFMTR00478; Xenodon pulcher: MNHNP11190, MNHNP2561, MNHNP10576, 1152 MNHNP4028, MNHNP2566, MNHNP10161, MNHNP3503, MACN44678; Xenodon rhabdocephalus: UFMTR04621, UFMTR03599, UFMTR03587, 1153 UFMTR03592, UFMTR03586; Xenodon severus: UFMTR00726, UFMTR02176, UFMTR03662, UFMTR03940; Xenopholis werdingorum: UFMTR2106, 1154 UFMTR00665, UFMTR01193, UFMTR01191, UFMTR11538, ZUFMS-CEUCH03676. 1155
181 1156 Table 3. Consume of eight discrete prey categories by Caenophidia snakes from Paraguay River Basin. cae_amph: caecilians and amphisbaenians.
snakes cae_amph fish anurans lizards mammals birds invertebrates Apostolepis ambiniger 0 1 0 0 0 0 0 0 Apostolepis assimilis 0 1 0 0 0 0 0 0 Apostolepis dimidiata 0 1 0 0 0 0 0 0 Apostolepis intermedia 0 1 0 0 0 0 0 0 Apostolepis nigroterminata 0 1 0 0 0 0 0 0 Apostolepis vittata 0 1 0 0 0 0 0 0 Atractus albuquerquei 0 0 0 0 0 0 0 1 Atractus paraguayensis 0 0 0 0 0 0 0 1 Boiruna maculata 1 0 0 0 0 0 0 0 Bothrops alternatus 0 0 0 0 0 1 0 0 Bothrops diporus 0 0 0 1 1 1 0 0 Bothrops mattogrossensis 0 0 0 1 1 1 0 0 Bothrops moojeni 1 0 0 1 1 1 1 1 Bothrops pauloensis 1 0 0 1 1 1 1 1 Chironius bicarinatus 0 0 0 1 0 0 0 0 Chironius exoletus 0 0 0 1 0 0 0 0 Chironius flavolineatus 0 0 0 1 0 0 0 0 Chironius laurenti 0 0 0 1 0 0 0 0 Chironius quadricarinatus 0 0 0 1 1 0 1 0 Chironius scurrulus 0 0 0 1 1 0 0 0 Clelia clelia 1 0 0 0 0 0 0 0 Clelia plumbea 1 0 0 0 0 0 0 0 Crotalus durissus 0 0 0 0 0 1 0 0 Dipsas indica 0 0 0 0 0 0 0 1
182 snakes cae_amph fish anurans lizards mammals birds invertebrates Drymarchon corais 1 0 1 1 1 1 1 0 Drymoluber brazili 0 0 0 0 1 0 0 0 Erythrolamprus aesculapii 1 0 0 0 0 0 0 0 Erythrolamprus albertguentheri 0 0 0 1 0 0 0 0 Erythrolamprus almadensis 0 0 0 1 0 0 0 0 Erythrolamprus frenatus 0 0 1 1 0 0 0 0 Erythrolamprus jaegeri 0 0 0 1 0 0 0 0 Erythrolamprus maryellenae 0 0 1 1 0 0 0 0 Erythrolamprus miliaris 0 0 1 1 0 0 0 0 Erythrolamprus poecilogyrus 0 0 0 1 0 0 0 0 Erythrolamprus reginae 0 0 0 1 0 0 0 0 Erythrolamprus sagittifer 0 0 0 1 0 0 0 0 Erythrolamprus semiaureus 0 0 1 1 0 0 0 0 Erythrolamprus taeniogaster 0 0 1 1 0 0 0 0 Erythrolamprus typhlus 0 0 0 1 0 0 0 0 Helicops angulatus 0 0 1 0 0 0 0 0 Helicops infrataeniatus 0 0 1 1 0 0 0 0 Helicops leopardinus 0 0 1 1 0 0 0 0 Helicops modestus 0 0 1 1 0 0 0 0 Helicops polylepis 0 0 1 0 0 0 0 0 Hydrodynastes bicinctus 1 0 1 1 1 1 0 0 Hydrodynastes gigas 1 0 1 1 0 1 0 0 Hydrops caesurus 0 0 1 0 0 0 0 0 Imantodes cenchoa 0 0 0 0 1 0 0 0 Leptodeira annulata 0 0 0 1 0 0 0 0
183 snakes cae_amph fish anurans lizards mammals birds invertebrates Leptophis ahaetulla 0 0 0 1 0 0 0 0 Lygophis dilepis 0 0 0 1 0 0 0 0 Lygophis flavifrenatus 0 0 0 1 1 0 0 0 Lygophis meridionalis 0 0 1 1 0 0 0 0 Lygophis paucidens 0 0 0 0 1 0 0 0 Mastigodryas bifossatus 0 0 0 1 0 0 0 0 Mastigodryas boddaerti 0 0 0 0 1 0 0 0 Micrurus baliocoryphus 1 0 0 0 1 0 0 0 Micrurus diana 1 0 0 0 1 0 0 0 Micrurus frontalis 1 0 0 0 1 0 0 0 Micrurus lemniscatus 1 1 1 0 1 0 0 0 Micrurus pyrrhocryptus 1 0 0 0 1 0 0 0 Micrurus surinamensis 0 0 1 0 0 0 0 0 Micrurus tricolor 1 0 0 0 0 0 0 0 Mussurana bicolor 1 0 0 1 0 0 0 0 Oxybelis aeneus 0 0 0 0 1 0 0 0 Oxybelis fulgidus 0 0 0 0 1 0 1 0 Oxyrhopus guibei 0 0 0 0 1 1 0 0 Oxyrhopus petolarius 0 0 0 0 1 1 1 0 Oxyrhopus rhombifer 0 0 0 0 1 1 0 0 Oxyrhopus trigeminus 0 0 0 0 1 1 0 0 Phalotris matogrossensis 0 1 0 0 0 0 0 0 Phalotris mertensi 0 1 0 0 0 0 0 0 Phalotris nasutus 1 0 0 0 0 0 0 0 Phalotris nigrilatus 1 0 0 0 0 0 0 0
184 snakes cae_amph fish anurans lizards mammals birds invertebrates Phalotris tricolor 1 0 0 0 0 0 0 0 Philodryas aestiva 0 0 0 1 0 0 0 0 Philodryas agassizii 0 0 0 0 0 0 0 1 Philodryas baroni 0 0 0 0 0 1 1 0 Philodryas livida 0 0 0 1 1 1 1 0 Philodryas mattogrossensis 0 0 0 1 1 1 0 0 Philodryas nattereri 0 0 0 1 1 1 1 0 Philodryas olfersii 0 0 0 0 0 1 1 0 Philodryas patagoniensis 1 0 0 1 1 1 1 1 Philodryas psammophidea 0 0 0 0 1 1 0 0 Philodryas viridissima 0 0 0 1 1 1 1 0 Phimophis guerini 0 0 0 0 1 0 0 0 Phimophis vittatus 0 0 0 0 1 0 0 0 Phrynonax poecilonotus 0 0 0 0 0 0 1 0 Pseudoboa coronata 1 0 0 0 1 1 0 0 Pseudoboa nigra 0 0 0 0 1 0 0 0 Pseudoeryx plicatilis 0 0 1 1 0 0 0 0 Psomophis genimaculatus 0 0 0 1 1 0 0 0 Rhachidelus brazili 0 0 0 0 0 0 1 0 Sibynomorphus lavillai 0 0 0 0 0 0 0 1 Sibynomorphus mikanii 0 0 0 0 0 0 0 1 Sibynomorphus turgidus 0 0 0 0 0 0 0 1 Sibynomorphus ventrimaculatus 0 0 0 0 0 0 0 1 Simophis rhinostoma 0 0 0 1 0 0 0 0 Siphlophis compressus 0 0 0 0 1 0 0 0
185 snakes cae_amph fish anurans lizards mammals birds invertebrates Spilotes pullatus 0 0 0 1 1 1 1 0 Spilotes sulphureus 0 0 0 0 1 1 1 0 Taeniophallus occipitalis 0 0 0 1 1 0 0 0 Tantilla melanocephala 0 0 0 0 0 0 0 1 Thamnodynastes chaquensis 0 0 0 1 0 0 0 0 Thamnodynastes hypoconia 0 0 0 1 0 0 0 0 Thamnodynastes lanei 0 0 0 1 0 0 0 0 Thamnodynastes rutilus 0 0 0 1 1 0 0 0 Xenodon matogrossensis 0 0 0 1 0 0 0 0 Xenodon merremii 0 0 0 1 0 0 0 0 Xenodon nattereri 0 0 0 0 1 0 0 0 Xenodon pulcher 0 0 0 1 0 0 0 0 Xenodon rhabdocephalus 0 0 0 1 0 0 0 0 Xenodon severus 0 0 0 1 0 0 0 0 Xenopholis undulatus 0 0 0 1 0 0 0 0 Xenopholis werdingorum 0 0 0 1 0 0 0 0
1157 Bibliografical references consuted to describe species diet: 1158 Achaval F, Olmos A. Anfibios y reptiles del Uruguay. Montevideo: Facultad de Ciencias; 1997. 1159 Alencar LRV. Ecomorfologia em serpentes neotropicais: um estudo de caso com a tribo Pseudoboini. M. Sc. Thesis, Universidade de São Paulo. 2010. 1160 Alencar LRV, Galdino CAB, Nascimento LB. Life history aspects of Oxyrhopus trigeminus (Serpentes: Dipsadidae) from two sites in southeastern Brazil. J 1161 Herpetol. 2012; 46:9-13.
186 1162 Almeida-Santos SM, Germano VJ. Crotalus durissus (Neotropical Ratlesnake). Prey. Herpetol Rev. 1996; 27:255-255. 1163 Amaral A. Contribuição à biologia dos ofídios Brasileiros. I-II. Nota previa. Coll Trab Inst Butantan. 1918; 2:1. 1164 Amaral A. Contribuição à biologia dos ophidios brasileiros (habitat, hábitos e alimentação). Coll Trab Inst Butantan. 1924; 2:177-181. 1165 Amaral A. Curiosos Habitos e Particularidades da Boipeva (Xenodon merremii: Colubridae). Boletim Biologico, Orgão do Clube Zoologico do Brasil. 1934; 2: 1166 1-2. 1167 Amaral A. Serpentes do Brasil: Iconografia Colorida. São Paulo: Universidade de São Paulo; 1978. 1168 Starace F. Guide des Serpents et Amphisbènes de Guyane. Guadeloupe: Ibis Rouge; 1998. 1169 Andrade DV, Abe AS, Santos MC. Is the venom related to diet and tail color during Bothrops moojeni ontogeny? J Herpetol. 1996; 30:285-288. 1170 Ávila-Pires TC. Lizards of Brazilian Amazônia (Reptilia: Squamata). The Netherlands: Zoologische Verhandelingen Zoologische Nationaal Natuurhistorisch 1171 Museum Leiden; 1995. 1172 Ávila RW, Porfírio GEO. Bothrops moojeni (Brazilian Lancehead). Predation. Herpetol Rev. 2008; 39:467-467. 1173 Bailey JR, Thomas RA, Silva NJ. A Revisiono f the South American snake genus Thamnodynastes Wagler, 1830 (Serpentes, Colubridae, Tachymenini). I. Two 1174 new species of Thamnodynastes from Central Brazil and adjacent areas, with a redefinition of and neotype designation for Thamnodynastes pallidus 1175 (Linnaeus, 1758). Phyllomedusa. 2005; 4:83-101. 1176 Barbo FE, Marques OAV, Sawaya RJ. Diversity, natural history, and distribution of snakes in the municipality of São Paulo. South Am J Herpetol. 2011: 6:135- 1177 160. 1178 Barbosa AR, Nishida AK, Costa ES, Cazé ALR. Abordagem etnoherpetológica de São José da Mata – Paraíba – Brasil. Revista de Biologia e Ciências da Terra. 1179 2007; 7:117-123. 1180 Beebe W. Field notes on the snakes of Kartabo, British Guiana, and Caripito, Venezuela. Zoologica. 1946; 31:11-52. 1181 Bernarde PS, Abe AS. A Snake Comunity at Espigão do Oeste, Rondônia, Southwestern Amazon, Brazil. South Am J Herpetol. 2006; 1:102-113.
187 1182 Bernarde PS, Abe AS. Hábitos alimentares de serpentes em Espigão do Oeste, Rondônia, Brasil. Biota Neotropica. 2010; 10:167-172. 1183 Bernarde PS, Machado MA. Oxyrhopus petola digitalis (NCN). Prey. Herpetol Rev. 2000; 31:247-248. 1184 Brites VLC. Ofiofagia de Bothrops moojeni Hoge, 1966 (Ophidia, Viperidae) na natureza. Uberlândia: Anais do Congresso científico da UFU 1; 1992. 1185 Campbell JA. Amphibians and reptiles of northern Guatemala, the Youcatán and Belize. Oklahoma: The University of Oklahoma; 1998. 1186 Cantor M, Pizzatto L. Leptodeira annulata (Banded Cat-Eyed Snake). Diet. Herpetol Rev. 2008; 39: 470-471. 1187 Carreira Vidal S. Alimentación de los ofidios de Uruguay. Montevideo: Asociación Herpetológica Española. Monografías de Herpetología; 2002. 1188 Carvalho CM, Vilar JC, Oliveira FF. Répteis e Anfíbios. In: Carvalho CM, Vilar JC, editors. Parque Nacional Serra de Itabaiana - Levantamento da Biota. São 1189 Cristóvão: UFS and Aracaju: Ibama; 2005. pp. 39-61. 1190 Carvalho JA. Diversidade de Serpentes do Parque Ecológico Quedas do Rio Bonito, Lavras, MG. M. Sc. Thesis, Universidade Federal de Lavras; 2006. 1191 Carvalho MA, Nogueira F. Serpentes da área urbana de Cuiabá, Mato Grosso: aspectos ecológicos e acidentes ofídicos associados. Cadernos de Saúde 1192 Pública. 1998; 14:753-763. 1193 Carvalho MA. Composição e História Natural de uma Comunidade e Serpentes em area de Transição Amazônia-Cerrado, Ecorregião Florestas Secas de Mato 1194 Grosso, Brasil. PhD. Thesis, Pontifícia Universidade Católica do Rio Grande do Sul; 2006. 1195 Cassimiro J, Bertoluci J. Natural history notes. Liophis maryellenae (Cobra d’a´gua). Diet. Herpetol Rev. 2003; 34:69-69. 1196 Cechin SZ. História natural de uma comunidade de serpentes na região da depressão central (Santa Maria) Rio Grande do Sul, Brasil. PhD. Thesis, Pontífica 1197 Universidade Católica do Rio Grande do Sul; 1999. 1198 Condez TH, Sawaya RJ, Dixo M. Herpetofauna dos remanescentes de Mata Atlântica da região de Tapiraí e Piedade, SP, sudeste do Brasil. Biota Neotropica. 1199 2009;9:157-185. 1200 Cunha OR, Nascimento FP. Ofídios da Amazônia XIX. As espécies de Oxyrhopus Wagler, com uma subespécie nova, e Pseudoboa Schneider, na Amazônia 1201 Oriental e Maranhão (Ophidia: Colubridae). Boletim do Museu Paranaense Emílio Goeldi. 1983; 1:1-42.
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191 1261 Mesquita PCMD, Borges-Nojosa DM, Monteiro FAC. Philodryas nattereri (Paraguay Green Racer). Diet. Herpetol Rev. 2010; 41:96-96. 1262 Michaud EJ, Dixon JR. Prey items of 20 species of the Neotropical colubrid snake genus Liophis. Herpetol Rev. 1989; 20:39-41. 1263 Monteiro C, Montgomery CE, Spina F, Sawaya RJ, Martins M. Feeding, reproduction, and morphology of Bothrops mattogrossensis (Serpentes, Viperidae, 1264 Crotalinae) in the Brazilian Pantanal. J Herpetol. 2006; 40:408-413. 1265 Mount RH. The Reptiles and Amphibians of Alabama. Auburn: Auburn University; 1975. 1266 Murphy JC. Amphibians and reptiles of Trinidad and Tobago. Malabar: Krieger Publishing Company; 1997. 1267 Myers CW, Cadle JE. A new genus for South American snakes related to Rhadinaea obtusa Cope (Colubridae) and resurrection of Taeniophallus Cope for the 1268 "Rhadinaea" brevirostris group. Am Mus Novitates. 1994; 3102: 1-33. 1269 Nascimento FP, Cunha OR, Ávila-Pires TCS. 1987. Os répteis da área do Carajás, Pará, Brasil (Squamata) II. Boletim do Museu Paraense Emílio Goeldi, serie 1270 Zoologia. 1994; 3:33- 65. 1271 Nogueira C, Sawaya RJ, Martins M. Ecology of the pitviper, Bothrops moojeni, in the Brazilian Cerrado. J Herpetol. 2003; 37:653-659. 1272 Palmuti CFS, Cassimiro J, Bertoluci J. Food habits of snakes from the RPPN Feliciano Miguel Abdala, an Atlantic Forest fragment of southeastern Brazil. Biota 1273 Neotropica. 2009; 9:263-269. 1274 Pavan D. Assembléias de répteis e anfíbios do Cerrado ao longo da bacia do rio Tocantins e o impacto do aproveitamento hidrelétrico da região na sua 1275 conservação. PhD. Thesis, Universidade de São Paulo; 2007. 1276 Pinto CC, Lema T. Comportamento alimentar e dieta de serpentes, gêneros Boiruna e Clelia (Serpentes, Colubridae). Iheringia, Ser Zool. 2002; 92:9-19. 1277 Pinto RR. Biologia reprodutiva e dieta de Chironius flavolineatus (Jan, 1863) e Chironius quadricarinatus (Boie, 1827) no Brasil (Serpentes: Colubridae). M.Sc. 1278 thesis, Universidade Federal do Rio de Janeiro; 2006. 1279 Pinto RR, Gomes M, Carvalho Jr R. Micrurus surinamensis (Aquatic Coralsnake). Ophiophagy. Herpetol Rev. 2011; 42: 442-442.
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194 1319 Vitt LJ. Diversity of reproductive strategies among Brazilian lizards and snakes: the significance of lineage and adaptation. In: Hamlett WC, editor. 1320 Reproductive Biology of South American Vertebrates. New York: Springer-Verlag; 1992. 1321 Yuki RN, Galatti U, Rocha RAT. Contribuição ao conhecimento da fauna de Squamata de Rondônia, Brasil, com dois novos registros. Boletim do Museu 1322 Paranaense Emílio Goeldi. 1999; 15:181-193. 1323 Yanosky AA, Dixon JR, Mercolli C. Ecology of the snake community at El Bagual Ecological Reserve, Northeastern Argentina. Herpetological Natural. 1996; 1324 4:97-110. 1325 Zweifel RG, Norris KS. Contribution to the herpetology of Sonora, Mexico: descriptions of new subspecies of snakes (Micruroides euryxanthus and 1326 Lampropeltis getulus) and miscellaneous collecting notes. American Midland Naturalist. 1955; 54:230-249
195 1327 Composite phylogeny of sankes from Paraguay River Basin
1328 The composite phylogeny used herein is based on previous studies from different
1329 authors and include all species registered to the Paraguay River Basin. Tonini et al. 2016 [1]
1330 and Pyron et al. 2013 [2] were used for the relative placement of snake families, subfamilies
1331 and tribes. Phylogenetic placement of snake species of Paraguay River basin that were not
1332 included in the available phylogenetic hypotheses were inferred according to the
1333 relationships of sister species or included as polytomies in nodes containing their closely
1334 related species. The phylogenies of Kluge 1991 [3], Rivera et al. 2011 [4], and Reynolds et al.
1335 2014 [5] were used for assessing relationships within Boidae. Within Viperidae, the
1336 phylogenies of Fenwick et al. 2009 [6] and Carrasco et al. 2012 [7] were used to determine
1337 the overall relationships among Bothrops species, whereas Machado et al. 2014 [8] was used
1338 for the relationships within Bothrops neuwiedii group. The relationships of species of
1339 Elapidae were determined using the phylogeny of Silva and Sites 2001 [9], with the position
1340 of Micrurus annellatus determined according to Slowinsky 1995 [10]. The relationships
1341 within Colubridae follow Klaczko et al. 2014 [11]. The overall relationships within Dipsadidae
1342 follow Grazziotin et al. 2012 [12], with the relationships within Pseudoboini following H.
1343 Zaher (USP, São Paulo; personal communication).
1344 REFERENCES
1345 1. Tonini JFR, Beard KH, Ferreira RB, Jetz W, Pyron A. Fully-sampled phylogenies of 1346 squamates reveal evolutionary patterns in threat status. Biol Conserv. 2016; Available: 1347 http://dx.doi.org/10.1016/j.biocon.2016.03.039 1348 2. Pyron RA, Burbrink FT, Wiens JJ. A phylogeny and revised classification of Squamata, 1349 including 4161 species of lizards and snakes. BMC Evol Biol. 2013; 13:93. 1350 3. Kluge AG. Boinae snake phylogeny and research cycles. Misc publ - Mus Zool, Univ 1351 Mich. 1991; 178:1-58.
196 1352 4. Rivera PC, Di Cola V, Martínez JJ, Gardenal CN, Chiaraviglio M. Species delimitation 1353 in the continental forms of the genus Epicrates (Serpentes, Boidae) integrating 1354 phylogenetics and environmental niche models. PLoS ONE. 2011; 6:e22199. 1355 5. Reynolds RG, Niemiller ML, Revell LJ. Toward a Tree-of-Life for the boas and 1356 pythons: multilocus species-level phylogeny with unprecedented taxon sampling. Mol 1357 Phylogenet Evol. 2014; 37:01-213. 1358 6. Fenwick AM, Gutberlet RL, Evans JA, Parkinson CL. Morphological and molecular 1359 evidence for phylogeny and classification of South American pitvipers, genera Bothrops, 1360 Bothriopsis, and Bothrocophias (Serpentes: Viperidae). Zool J Linn Soc. 2009; 156:617-640. 1361 7. Carrasco PA, Mattoni CI, Leynaud GC, Scrocchi GJ. Morphology, phylogeny and 1362 taxonomy of South American bothropoid pitvipers (Serpentes, Viperidae). Zool Scripta. 1363 2012; 41:109-124. 1364 8. Machado T, Silva VX, Silva MJ. Phylogenetic relationships within Bothrops neuwiedii 1365 group (Serpentes, Squamata): geographically highly-structured lineages, evidence of 1366 introgressive hybridization and Neogene/Quaternary diversification. Mol Phylogenet Evol. 1367 2014; 71:1-14. 1368 9. Silva NJ, Sites JW. Phylogeny of South America triad coral snakes (Elapidae: 1369 Micrurus) based on molecular characters. Herpetologica. 2001; 57:1-22. 1370 10. Slowinski JB. A phylogenetic analysis of the New World coral snakes (Elapidae: 1371 Leptomicrurus, Micruroides, and Micrurus) based on allozyme and morphological characters. 1372 J Herpetol. 1995; 29:325-338. 1373 11. Klaczko J, Montingelli GG, Zaher H. A combined morphological and molecular 1374 phylogeny of the genus Chironius Fitzinger, 1826 (Serpentes: Colubridae). Zool J Linn Soc. 1375 2014; 171:656-667. 1376 12. Grazziotin FG, Zaher H, Murphy RW, Scrocchi G, Benavides MA, Zhang YP et al. 1377 Molecular phylogeny of the New World Dipsadidae (Serpentes: Colubroidea): a reappraisal. 1378 Cladistics. 2012; 28: 437-459 1379
197 1380
1381 Figure 2. Composite phylogeny of the 155 snake species from the Paraguay River Basin. To 1382 improve the visualization this representation ignored the branch length.
198 1383 S2 Appendix. Values of phylogenetic and functional structure of snake metacommunities,
1384 and additional relations of communities’ functional diversity with environmental gradients
1385 at Paraguay River Basin (PRB).
1386 Table 1. Values of species richness, seasonal flooded cover, forest cover and indexes of 1387 structure phylogenetic and functional from 31 snakes communities from Paraguay River 1388 Basin. Bolded values were statistically significant considering α = 0.05 (p ≤ 0.025 ou p ≥ 1389 0.975). FRic FDis FRic FDis FRic FDis FRic FDis Com Rich Flood Forest NRI NTI MOpt MOpt MN MN DOpt DOpt DN DN 1 53 0 9.67 -1.06 -1.15 -0.20 -0.24 0.53 0.21 -0.23 0.11 1.42 2.07 2 37 0 18.29 -1.44 -2.56 1.13 0.75 2.22 -0.24 0.49 -1.51 0.19 0.90 3 61 0 4.45 -1.02 -0.22 -0.11 -0.94 0.58 0.58 -0.42 -0.68 -0.12 1.14 4 37 0 9.88 -0.95 -1.07 -0.86 -0.73 0.36 -0.06 0.46 0.71 -0.82 0.19 5 34 11.11 25.06 -0.71 -0.47 0.97 1.54 0.39 1.08 -0.92 -0.88 -0.61 -0.17 6 42 44.44 9.85 0.17 -0.24 -0.01 -0.80 -0.98 -0.82 0.17 -0.27 -0.15 -0.78 7 19 100 29.91 0.46 0.14 0.89 1.76 -0.32 -0.68 -0.47 -1.37 -0.22 -0.69 8 20 100 43.53 1.43 0.65 0.78 1.80 -0.42 -0.29 -0.64 -1.92 0.59 -0.27 9 34 22.22 8.81 -0.30 -1.01 0.48 0.57 1.45 0.89 0.57 1.04 0.45 0.77 10 37 77.78 46.36 -0.11 -2.01 1.19 1.28 0.83 0.26 0.45 -0.30 1.27 0.27 11 15 0 68.37 -1.78 -2.18 -0.69 -0.41 -0.26 1.10 0.05 -0.10 1.32 0.55 12 17 0 48.77 -0.99 -0.98 -1.08 -1.15 0.15 0.28 -1.23 0.21 -0.90 -0.40 13 52 77.78 31.22 -0.34 -0.62 0.60 0.08 0.91 0.52 -0.16 -0.10 0.40 -0.94 14 32 100 3.94 0.14 -1.23 -1.16 -1.35 0.25 -1.60 -0.82 -1.24 -2.69 -3.11 15 18 56.45 40.56 -0.73 -1.49 0.34 -0.48 0.01 -0.04 -1.43 -0.88 -0.03 -1.50 16 31 88.89 15.73 -0.95 -0.49 0.48 0.89 -0.46 -0.11 -0.75 -0.02 0.67 -0.24 17 31 100 21.78 -1.28 0.33 0.28 0.43 -1.26 -1.36 -0.75 1.34 -2.50 -0.72 18 51 100 13.71 -0.12 -1.02 0.32 0.30 -0.16 -1.22 -0.20 0.92 0.48 -0.16 19 16 0 76.24 -1.30 -0.29 -1.27 -1.20 -0.98 0.01 -0.05 -0.08 -0.80 -0.54 20 18 100 12.87 0.25 0.19 1.00 1.05 -0.36 -0.52 0.71 1.58 1.86 1.32 21 28 11.11 36.94 -1.01 -0.69 -1.93 -2.31 -1.24 -1.08 -1.75 -0.07 -0.14 0.28 22 54 44.44 42.43 1.09 0.27 0.46 -0.18 1.27 -0.22 -0.30 -0.35 1.42 -0.03 23 49 11.11 11.91 0.02 -1.13 0.33 -0.22 0.96 0.82 -0.08 1.29 0.51 0.29 24 32 0 10.86 0.67 0.68 0.30 0.36 0.95 0.93 -0.80 0.03 -1.55 0.02 25 24 0 7.50 -1.85 -0.67 0.16 0.34 -0.19 0.28 1.16 1.46 1.23 1.95 26 25 44.44 35.26 -0.68 -0.42 0.02 -0.27 -0.25 -0.65 -0.13 0.27 -0.90 -0.86 27 30 0 13.83 -1.88 -0.89 -0.34 0.01 -0.50 0.43 0.81 0.10 0.77 -0.01 28 21 0 64.89 0.02 0.36 -1.43 -1.63 -1.03 -0.76 -1.88 -1.17 -1.52 -1.19 29 23 0 29.99 -1.70 -0.14 0.30 1.10 0.21 1.73 1.28 1.64 0.30 1.79 30 30 11.11 25.57 -0.29 -1.64 -0.68 -0.98 -0.19 -0.30 0.77 -0.58 -0.28 -0.97 31 37 33.33 15.32 0.11 -0.52 -1.33 -1.00 -0.36 -0.29 0.41 0.53 -1.95 -3.01
199 1390 Com: community identification; Rich: species richness; Flood: cover of seasonal flooded area; Forest: cover 1391 of forested area; NRI: net relatedness taxon index; NTI: nearest taxon index; FRic MOpt: standardized values 1392 of FRic considering morphological traits that optimized the relation with environmental variables; FDis MOpt: 1393 standardized values of FDis considering morphological traits that optimized the relation with environmental 1394 variables; FRic MN: standardized values of FRic considering the other morphological traits; FDis MN: 1395 standardized values of FDis considering the other morphological traits; FRic DOpt: standardized values of 1396 FRic considering the consume of dietary items that optimized the relation with environmental variables; FDis 1397 DOpt: standardized values of FDis considering the consume of dietary items that optimized the relation with 1398 environmental variables; FRic DN: standardized values of FRic considering the consume of the others dietary 1399 items; and FDis DN: standardized values of FDis considering the consume of the others dietary items.
1400
1401 Figure 1. Relations of standardized values of functional space filled by the community 1402 (SESFRic) and dispersion of the species in the multifunctional space (SESDis) with 1403 environmental gradients, considering morphological traits that are linked with habitat use 1404 by snakes species at Paraguay River Basin. Black closed points and grey line show the 1405 relations with flooding gradient and open points and dashed line show the tendencies 1406 regarding the forest cover gradient. Coefficient f: flooding cover; coefficient c: forest cover.
200
1407
1408 Figure 2. Relations of standardized values of functional space filled by the community 1409 (SESFRic) and dispersion of the species in the multifunctional space (SESDis) with 1410 environmental gradients, considering diet of snakes species at Paraguay River Basin. Black 1411 closed points and grey line show the relations with flooding gradient and open points and 1412 dashed line show the tendencies regarding the forest cover gradient. Coefficient f: flooding 1413 cover; coefficient c: forest cover.
201 1414
1415 Figure 3. Relations of community-weighted means with environmental gradients. Black 1416 closed points and grey line show the relations with flooding gradient and open points and 1417 dashed line show the tendencies regarding the forest cover gradient. Coefficient f: flooding 1418 cover; coefficient c: forest cover.
202 1419
1420 Figure 4. Relations of frequency of consume of alimentary items by species at communities 1421 and environmental gradients. Black closed points and grey line show the relations with 1422 flooding gradient and open points and dashed line show the tendencies regarding the forest 1423 cover gradient. Coefficient f: flooding; coefficient c: forest.
203 1 FINAL CONSIDERATIONS
2 By gathering records of snake occurrence in the Pantanal and surrounding areas and
3 investigating the taxonomic, phylogenetic, and phenotypic diversity of communities we
4 aimed to better understand which processes contributed and currently act to shape these
5 communities in this floodplain. The chapters presented herein tested and originated new
6 hypotheses that formally addressed the widely invoked theory that seasonal floods are the
7 main ecological feature affecting biological communities in the Pantanal.
8 Our results about occurrence of distinct biogeographical units in the basin where the
9 Pantanal is located showed that regarding the snake fauna, the floodplain cannot be
10 considered a separated biogeographical region, neither was composed of regionalized
11 faunas found in the region. Rather, the Pantanal snake fauna is part of a species group
12 widely distributed in the study region, linked to the Paraguay River channel and nearby
13 lowland areas. We also found evidence of a vicariant role of the Pantanal origin on the
14 ancestral fauna of the region – the distribution of the regionalized fauna agrees with the
15 hypothesis that some previous species ranges may have been split or limited when the
16 floodplain arose. From these results we can infer that the present snake communities in the
17 Pantanal were not constrained by in situ evolutionary processes, but are assemblages of
18 species formed from a regional pool composed by faunas with diverse history and
19 distribution. Furthermore, the Pantanal may currently act as a barrier for some species of
20 this pool and as a dispersal corridor for others.
21 When we evaluated what factors drive beta diversity patterns among communities
22 in different areas in and around the Pantanal, forest cover was the variable that explained
23 most of the difference in composition between pairs of communities. Furthermore, beta
24 diversity was also influenced by spatial process and climatic variables, which are important
25 for snake physiology. Regarding the effect of seasonal floods, we found that it mainly
26 influenced the turnover of species between assemblages. Floods seemed to be interacting
204 27 with other environmental features and could be limiting the range of some species that do
28 not show adaptations for recurrent and seasonal flooding that bring large alterations to the
29 environments. These results helped us to identify through which environmental features the
30 forces that currently shape local communities in the Pantanal can be operating.
31 Lastly, we investigated how the phylogenetic and phenotypic structure of local
32 communities inside and around the Pantanal were correlated with the gradient of forest
33 cover and flood intensity. Our expectation that seasonal flooding could act as an
34 environmental filter was not supported. For the most part, local assemblages from flooded
35 areas were randomly structured from the regional pool and did not show morphological
36 convergence regarding specific traits that theoretically improve the use of flooded
37 environments. Contrarily, those communities were composed of species with morphological
38 divergence regarding traits related to aquatic habits, while showing a higher similarity in
39 body shape. Based on these results, we suspect that seasonal flooding, besides favoring the
40 occurrence of aquatic species, is decreasing the relative force of deterministic processes on
41 community assembly and can be favoring species with generalist habits by promoting
42 recurrent ecosystem disturbances. We also provide evidence that an environmental filter
43 can be acting through the forest cover gradient. More forested areas had lower species
44 richness and showed morphological convergence, but they did not show lower functional
45 diversity when compared to open areas. This means that historical divergences among the
46 regional pool of different local communities may also have originated the observed pattern,
47 rather than an isolated action of environmental filtering.
48 Overall, the present dissertation showed that the composition of snake communities
49 in the Pantanal is influenced by seasonal flooding and by other environmental (forest cover),
50 climatic (minimum temperature), and historical factors. Seasonal flooding seems not to act
51 on species as an environmental filter that shape local communities in a specific way. At least
52 for these mobile organisms, the main role of flooding is producing recurrent disturbances in
205 53 the ecosystems, therefore increasing the effect of random processes on the assembly of
54 communities. Furthermore, our study provided a set of new hypotheses to be addressed
55 and extended to other seasonally flooded areas, what can contribute to a better
56 understanding of the origins and maintenance of the biota in the Pantanal and other similar
57 regions.
206 INDEX
Resumo ……………………………………………………………………………………………………………..…… 1
Abstract …………………………………..……………………………………………………………………………… 2
General Introdution ………………………………………………..……………………………………………… 3
Chapter 1 – The role of the Pantanal floodplain in the biogeographical patterns of
snakes in the Paraguay River Basin, central South America ……………………………… 11
Abstract ……………………………………………………………………………………………………. 12
Introduction ……………………………………………………………………………………………… 13
Materials and Methods ………………………..…………………………………………………… 15
Results ……………………….……………………………………………………………………………… 20
Discussion …………………………………………………………………………………………………. 23
References ………………………………………………………………………………………………… 28
S1 Appendix ……………………………………………………………………………………………… 35
S2 Appendix ……………………………………………………………………………………………… 37
S1 Table …………………………………………………………………………………………………… 42
Chapter 2 – Relative importance of flooding as driver of snakes species turnover in wetlands in central South America ……………………………………………………………………….. 74
Abstract ……………………………………………………………………………………………………. 75
Introduction ……………………………………………………………………………………………… 76
Materials and Methods …………………………………………………………………………….. 77
Results ………………………………………………………………………………………………………. 84
Discussion …………………………………………………………………………………………………. 89
References ………………………………………………………………………………………………… 94
S1 Table ………………………………………………………………………………………………….. 103
207 Chapter 3 – The role of seasonal flooding in assembling snake communities in the
Pantanal and surrounding areas …………………………………………………………………………. 113
Abstract ………………………………………………………………………………………………….. 114
Introduction ……………………………………………………………………………………………. 115
Materials and Methods …………………………………………………………………………… 118
Results ……………………………………………………………………………………………………. 131
Discussion ……………………………………………………………………………………………….. 139
References ……………………………………………………………………………………………… 146
S1 Appendix ……………………………………………………………………………………………. 157
S2 Appendix ……………………………………………………………………………………………. 199
Final Considerations …………………………………………………………………………………………… 204
208