Comunidades De Besouros Rola-Bostas (Coleoptera: Scarabaeinae): Do Macro a Ecologia De Indivíduos

UNIVERSIDADE FEDERAL DE GOIÁS INSTITUTO DE CIÊNCIAS BIOLÓGICAS PROGRAMA DE PÓS-GRADUAÇÃO EM ECOLOGIA E EVOLUÇÃO

COMUNIDADES DE BESOUROS ROLA-BOSTAS (COLEOPTERA: SCARABAEINAE): DO MACRO A ECOLOGIA DE INDIVÍDUOS

MARCELO BRUNO PESSÔA

Orientador: Dr. Joaquín Hortal Co-orientador: Dr. Paulo De Marco Júnior

Goiânia, Goiás Março, 2019

MARCELO BRUNO PESSÔA

COMUNIDADES DE BESOUROS ROLA-BOSTAS (COLEOPTERA: SCARABAEINAE): DO MACRO A ECOLOGIA DE INDIVÍDUOS

Tese apresentada à Universidade Federal de Goiás como parte das exigências do Programa de Pós- Graduação em Ecologia e Evolução para obtenção do título de doutor.

Orientador: Dr. Joaquín Hortal Co-orientador: Dr. Paulo De Marco Júnior

Goiânia, Goiás Março, 2019

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DEDICO

Ao meu avô, que em memória,

Será sempre eterno em minhas lembranças.

A minha família que entendeu

Que o caminho da ciência é árduo

E tão significante quanto outro trabalho

A minha esposa que sempre me levantou

Quando faltava ânimo

A todos os mestres da minha vida

Que foram os gigantes os quais eu subi nos ombros.

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AGRADECIMENTO

Primeiramente a Kephra, deus rola-bosta, que carreia o sol e renasce do pó. Os rola bostas que inspiram minha curiosidade científica. Muito chão percorri até chegar nesse momento. Um sonho infantil talvez, de tentar aprender sempre mais, de conhecer e desvendar novos mistérios. Um sonho desde criança de ser cientista. Incentivado por vários. Desde meu avô, in memorian, que me ensinou a fazer o que eu amasse. Minha família, que embora tenha demorado um pouco, entendeu que fazer ciência é uma coisa importante pra mim, e embora não seja um trabalho “convencional”, merece tanto respeito quanto os outros. Esse sonho foi possível porque essa vontade de conhecer mais foi incentivada pelos meus professores, desde o ensino fundamental, na graduação, mestrado e agora no doutorado. Estes foram os gigantes o qual subi aos ombros e agora ameaço voar. Agradeço aos meus “brothers of metal” Raphael e Lucas, o trio de loucos, que mesmo longe estão juntos. Mesmo o Rapha não entendendo nada quando eu e o Lucas discutia ele sempre tentava entender. Quando o assunto era nerd, aí discutiam os três, mas sempre com bom humor. Aos amigos que conheci nessa trajetória do doutorado. Lilian, Renan, Day, a vilinha ficou muito mais feliz depois que vocês chegaram e especialmente quando a gente fazia nossos jantares de corredor. Os amigos do Methaland, que me receberam muito bem, que renderam sessões de RPG, e jogos de tabuleiro. Aos amigos do “despacho” do Museo de Ciências Naturales de Madrid, em especial a Fernanda, ao Thiago e ao Jorge, embora tenham sido apenas 6 meses, foram muitos aprendizados. Aos orientadores Joaquín e Paulo, que foram sempre muito solícitos e humanos. Que entendiam que por trás do doutorando havia um humano que come, dorme, cansa, e precisa de momentos além da academia. E se cheguei longe nesse sonho foi porque nos últimos 8 anos dessa jornada tive a companhia de uma pessoa que me apoiou sem pedir nada em troca, que esteve do meu lado nos momentos tensos, e principalmente nos bons, que dormiu em aeroportos comigo, que conseguiu aproveitar um pouco da Europa e o sonho de conhecer a Alemanha, que me acompanha nas minhas nerdices e gulodices, minha esposa Tatiana Souza do Amaral, te amo! E por último, agradeço a CAPES, pela concessão da bolsa de doutorado, e pela bolsa sanduíche. A todos. Muito obrigado.

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“We must not forget that when radium was discovered no one knew that it would prove useful in hospitals. The work was one of pure science. And this is a

proof that scientific work must not be considered from the point of view of the

direct usefulness of it. It must be done for itself, for the beauty of science, and then there is always the chance that a scientific discovery may become like the

radium a benefit for humanity.”

“I am among those who think that science has great beauty. A scientist in his

laboratory is not only a technician: he is also a child placed before natural

phenomena which impress him like a fairy tale.”

Marie Curie

SUMÁRIO RESUMO ...... 16 ABSTRACT ...... 18 INTRODUÇÃO GERAL ...... 22 Organização da tese ...... 22 O gradiente de diversidade latitudinal ...... 23 Funções ecológicas ...... 28 História Natural dos Scarabaeinae ...... 34 Atributos Funcionais de Scarabaeinae ...... 43 Referências ...... 54 GENERAL INTRODUCTION ...... 61 Thesis organization...... 61 The Latitudinal Diversity Gradient ...... 62 Ecological Functions ...... 67 Dung Beetle Natural History ...... 72 Dung Beetle Functional Traits ...... 82 References ...... 97 CAPÍTULO 1 ...... 104 UNVEILING THE DRIVERS OF DUNG BEETLE LOCAL SPECIES RICHNESS IN THE NEOTROPICS .. 104 Abstract ...... 104 Introduction ...... 106 Methods ...... 108 Data collection and filtering ...... 108 Statistical analyses ...... 113 Results ...... 115 Multi-hypothesis testing ...... 116 Structural Equation Model ...... 117 Discussion ...... 118 Conclusions ...... 122 References ...... 123 Capítulo 2 ...... 129 ASSESSING THE GEOGRAPHICAL VARIATIONS IN THE DETERMINANTS OF DUNG BEETLE LOCAL SPECIES RICHNESS ACROSS THE NEOTROPICS ...... 129 Abstract ...... 129 Introduction ...... 131 Methods ...... 133

Data construction ...... 133 Predictor Variables ...... 134 Statistical analyses ...... 136 Results ...... 137 Discussion ...... 146 Conclusions ...... 149 References ...... 150 CAPÍTULO 3 ...... 157 FOREST CONVERSION INTO PASTURE SELECTS DUNG BEETLE TRAITS AT DIFFERENT BIOLOGICAL SCALES DEPENDING ON SPECIES POOL COMPOSITION ...... 157 Introduction ...... 158 Methods ...... 161 Study areas ...... 161 Dung beetle surveys ...... 162 Measuring dung beetle functional traits ...... 163 Functional diversity indices ...... 163 Statistical Analyses ...... 164 Results ...... 166 Discussion ...... 176 Conclusion ...... 179 References ...... 180 General Conclusions ...... 188 Conclusão Geral ...... 190 Supplementary Information S1 ...... 193 Supplementary Information S2 ...... 215 Supplementary Information S3 ...... 245

LISTA DE FIGURAS

Figure 1.1. Dung beetle nesting patterns and their evolutive trends as shown in Halffter & Edmonds, 1982 ...... 81 Figure 1.2: Morphological traits of dung beetles. A) Size, B) Protibia area, C) Prosternum Height, D) Mesotibia ratio, E) Metatibia length, F) Wing area, if you divide this by the size you have the wing load, G) antennal sensillas, H) Eye area - adapted from Pizzo et al., 2012, i) clypeaus width, J) Horn, K) mandibulae of a dung beetle, L) color variation of two species of dung beetles Coprophanaeus dardanus e Canthon podagricus...... 88 Figure 1.3: Behavioral traits of dung beetles. A) reallocation behavior, B) type of nest, C) number of eggs, D) burial depth, E) diet, F) habitat preference, G) thermoregulation. A, B, C and D modified from Doube, 1990. E and F from Hill, 1996. G modified from Verdú et al., 2012...... 92 Figure 1.4: Phenological Traits of Dung Beetles. A) Complete life cycle of dung beetles, B) Larval development time (the phases of 1 trought 4, until the emergence of the adult), C) Daily Activity, D) Seasonality. A and B from https://goo.gl/images/De50hb. C and D from Hernández, 2002...... 95 Figure 2.1: Location of all dung beetle studies in the Neotropics found through our literature search. In red, studies not used in the analysis and in black, studies used in the analysis...... 109 Figure 2.2: Prior conceptual model of the relationships between dung beetle richness and geographical gradients hypotheses variables...... 115 Figure 2.3 Dung beetle local species richness in the Neotropical Region. Increasing species richness is depicted in progressively larger circles and on a continuous scale from white (lowest richness) to red (highest richness). Species richness values have been Log10 transformed...... 116 Figure 2.4. Structural equation model for dung beetle richness on the Neotropics. The arrows indicate the significant paths with their respective standardized coefficients (numbers) Black arrows denote positive relationships and red arrows denote negative relationships...... 118 Figure 3.1. Location of the studies used in the analyses...... 134 Figure 3.2. Conceptual Model depicting the hypothesis about the relationships among the factors that affect Dung Beetle local Richness in the Neotropics, following Pessoa et al. (Chapter 1)...... 137 Figure 3.3. Frequency of Completeness scores of all sites of the dung beetles local community database considered as well-surveyed (completeness scores higher than 0.8)...... 137 Figure 3.4: Dung beetle Estimated Richness for each biome surveyed in the database of the Neotropics...... 138 Figure 3.5. Dung Beetle estimated richness for each surveyed site, the points are jittered in the map. The sizes of the circles represent the completeness of the sites...... 139 Figure 3.6. Main Drivers of dung beetle species richness in the neotropics. In A main drivers of Scarabaeinae. In B Main drivers of Scarabaeinae abundance. And in C Main drivers of Mammal S. Yellow points represent Abundance, blue points represent Climate2, red points represent Mammal S, green points represent Climate1, and white points represent Soil3 as the main driver. The sites are jittered...... 140 Figure 3.7. Summary of the Structural Equation Models hypothesis of Dung Beetle Richness Drivers. Red dots Meso-America region. In Green Amazonian Region. In Blue

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Subtropical formations. Black lines represent positive relations and red line negative. Dot-dashed lines represent relations where the region...... 141 Figure 3.8. Spatial variation of the strength of the relationships between dung beetles local richness (Scarab S) and different drivers of diversity, measured as the values of the betas coefficients in GWLMM structural equation models. The scale goes from red (negative values) to black(positive values). The location of the sites has been jittered to allow visualization of the nearby sites...... 142 Figure 3.9 Spatial variation of strength relations (betas) of dung beetle abundance (Abundance) with drivers of diversity. The scale goes from red (negative values) to black(positive values). The sites are jittered. Biomes color as previously legend...... 144 Figure 3.10. Spatial variation of strength relations (betas) of Mammal Richness (Mammal S) with drivers of diversity. The scale goes from red (negative values) to black (positive values). The sites are jittered...... 145 Figure 4.1: Location of the regions and areas utilized for dung beetle surveys. A Goiânia region. B Itajaí Valey region...... 161 Figure 4.2: A) Baited Pitfall trap used to collect the dung beetles. B) Trap placement design...... 162 Figure 4.3. Dung beetle functional traits measured in five individuals per habitat per area. 1. Dorsal Eye Area, 2. Head Length, 3. Head Width, 4. Pronotum Length, 5. Pronotum Width, 6. Elytra Length, 7. Protibia Area, 8. Metatibia Length, 9. Prosternum Height, 10. Wing Area. Body Length was calculated summing Pronotum Length and Elytra Length. Wing load was calculated by the ratio of wing area by body size. And Volume was calculated by multiplying Body Size, Pronotum Width, and Prosternum Height...... 165 Figure 4.4. Pcoa Axis for the dung beetles surveyed in the Goiânia and Itajaí Valley regions. Circles represent Goiânia, and triangles represent Itajaí Valley. In red Forest and in Green Pasture...... 170 Figure 4.5. Dung beetle species richness and functional diversity in forest and pasture habitats obtained in the surveys of Goiânia and Itajaí Valley Regions. Box plots show the average and interquartile range of site values; dots identify extreme values...... 171 Figure 4.6. Standardized effect size for the different regions and habitats. Circles are Goiânia Region, triangles Itajaí Valley. Green pastures, Red forests. The scales of the symbols represent the observed value of the index...... 173 Figure 4.7. Pcoa Axis for the CWM of dung beetles surveyed in the Goiânia and Itajaí Valley regions. Circles represent Goiânia, and triangles represent Itajaí Valley. In red Forest and in Green Pasture...... 174 Figure 4.8. Nested partition of dung beetle traits variance surveyed in forest patches and pastures in Itajaí Valley and Goiânia Region. Vol = Volume, Len = Length, W.Lo = Wing Load, Ps.H = Prosternum Height, Me.L = Metatibia Length, Pt.A = Protibia Area, Ey.A = Eye Dorsal Area, He.W = Head Width, He.L = Head Length, Pr.W = Pronotum Width...... 175 Figure 4.9. Standardized effect size of Traits Statistics obtained for each dung beetle traits in Goiânia Region ad Itajaí Valley for Forest patches and pasture. Asterisc represent significative values, blue=negative and orange=positive.Vol = Volume, Len = Length, W.Lo = Wing Load, Ps.H = Prosternum Height, Me.L = Metatibia Length, Pt.A = Protibia Area, Ey.A = Eye Dorsal Area, He.W = Head Width, He.L = Head Length, Pr.W = Pronotum Width. T_IP.IC=Internal Filtering of Individuals, T_IC.IR=External Filtering of individuals, T_PC.PR=External Filtering of Species...... 176 Figure S1.1. Log of Dung Beetle Richness and Abundance relation with Trap Hour. . 212 Figure S1.2. Scarabaeinae Richness significative path relations...... 213 Figure S1.3. Scarabaeinae Abundance significative paths relations ...... 213

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Figure S1.4 Mammal Richness significative paths relations...... 214 Figure S3.1. CWM of Dung Beetles for traits measured in forest patches and pasture for Goiânia and Itajaí Valley Region. In red Forest, in Blue Pasture ...... 252 Figure S3.2. Relation between Dung Beetles Species Richness and Trait Statistic (T_IP.IC) for traits measured in forest patches and pasture for Goiânia and Itajaí Valley Region. Vol = Volume, Len = Length, W.Lo = Wing Load, Ps.H = Prosternum Height, Me.L = Metatibia Length, Pt.A = Protibia Area, Ey.A = Eye Dorsal Area, He.W = Head Width, He.L = Head Length, Pr.W = Pronotum Width. T_IP.IC=Internal Filtering of Individuals...... 253

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LISTA DE TABELAS

Table 3.1. Hypotheses on the origin of geographic diversity gradients evaluated in this work, including the reference where they were originally proposed, the variables initially proposed or studied, and the variables used in this work to account for each one of them. AET stands for Actual Evapotranspiration, TNPP for Total Net Primary Production, and PET for Potential Evapotranspiration...... 112 Table 3.2. Multi-models result in isolated hypotheses and the most explicative combination. PP – Productivity hypothesis; WE – Water-Energy hypothesis; AH- Ambient Heterogeneity hypothesis; RH – Resource Heterogeneity hypothesis...... 117 Table 4.1. Dung beetle collected in Forest and Pasture in Cerrado...... 167 Table 4.2. Dung beetle collected in Forest and Pasture in Atlantic Forest...... 169 Table 4.3. Results of the linear mixed models for the effects of habitat and region (and their interaction) on species richness and functional diversity indices, and their standardized effect sizes (SES). S stands for species richness, FRich for functional richness, FEve for functional evenness and FDiv for functional divergence...... 171 Table 4.4. Results of the linear mixed models of the Community Weighted Mean of individual traits...... 174

1 RESUMO 2

3 O estudo dos padrões de diversidade tem gerado várias hipóteses, que se tem

4 visto usualmente como rivais. Mas a diversidade é um fenômeno complexo,

5 resultado dos efeitos de múltiplos fatores que atuam simultaneamentee que

6 podem variar no espaço. A variação do conjunto de determinantes que explicam

7 a diversidade local é importante para entender os efeitos das mudanças do uso

8 do solo nas comunidades. Esses fatores agem como diferentes filtros, mudando

9 a composição e estrutura da comunidadee até mesmo filtrando atributos dos

10 indivíduos, alterando também a estrutura funcional da comunidade. Definimos

11 três questões principais: (1) quais são os principais determinantes da riqueza

12 local de rola-bostas no Neotrópico; (2) como a importância relativa desses

13 fatores varia geograficamente; e como o tempo desde a conversão da floresta

14 em pasto afeta a estrutura funcional da comunidade.

15 Para a primeira questão, construímos um banco de dados de

16 comunidades de rola-bostas com literatura publicada para extrair informações

17 sobre riqueza de espécies, abundância, tipo de isca, tipo de habitat e esforço

18 amostral (horas-armadilha). Usamos uma abordagem multi-hipóteses para

19 entender qual é o conjunto de hipóteses que melhor explica a riqueza de rola-

20 bostas numa escala local. Em concreto, usamos variáveis ambientais para testar

21 seis hipóteses: produtividade, água-energia, energia ambiental,

22 heterogeneidade do habitat, heterogeneidade climática e heterogeneidade de

23 recursos. Testamos também hipótese neutra usando apenas dados espaciais.

24 Para a segunda questão compilamos dados de amostragens padronizadas de

25 armadilhas de queda e estimamos a riqueza de espécies em cada local usando

26 estimadores de cobertura de amostragem. Depois, analisamos a relação de

27 vários preditores (incluindo clima, habitat e diversidade de mamíferos) com a

28 riqueza de espécies, e também entre eles, através de modelos mistos de

29 equações estruturais, geograficamente ponderados. E para a terceira questão,

30 realizamos coletas padronizadas de comunidades de rola-bostas em sete

31 fragmentos florestais e pastagens adjacentes em duas diferentes regiões

32 pertencentes aos biomas Mata Atlântica (Vale do Itajaí) e Cerrado (região de

33 Goiânia), utilizando armadilhas de queda iscadas com fezes humanas e de vaca,

34 e fígado podre. Medimos catorze atributos de indivíduos coletados em cada tipo

35 de habitat em cada local específico. A partir dos valores da variação nesses

36 traços, calculamos a riqueza funcional, a uniformidade funcional, a divergência

37 funcional e a média ponderada pela comunidade das características de cada

38 área e analisamos a variação individual por meio de Trait Statistics.

39 Descobrimos que a riqueza local de rola-bostas é resultado da

40 produtividade (por energia e água) e Heterogeneidade (tanto de habitat quanto

41 de recurso). Após a análise das variáveis interpretamos que a hipótese de “mais-

42 indivíduos” é o principal mecanismo que conduz a diversidade de rola-bostas,

43 por meio da importância da abundância. Essa importância é comum a todo o

44 Neotrópico, mas os fatores que afetam a abundância variam entre regiões. A

45 diversidade de rola-bostas apresenta heterogeneidade geográfica nas respostas

46 aos fatores, onde podemos observar três regiões: Mesoamérica, Amazônica e

47 América do Sul Subtropical. Além disso, a diversidade de mamíferos contribuiu

48 para a diversidade e para a abundância de rola-bostas diferentemente,

49 principalmente como consequência da conversão de floresta em pastagens. A

50 conversão de floresta em pastagem afetou a diversidade funcional dos

51 escaravelhos, onde a pastagem apresentou menor riqueza funcional tanto no

52 Vale do Itajaí como em Goiânia. No entanto, o pool regional de espécies teve um

53 efeito maior que o tempo para a redução do efeito dessa conversão. A diferença

54 no conjunto de espécies também reflete na variação individual dos atributos.

55 Enquanto na Mata Atlântica a filtragem ocorre no nível da espécie, no Cerrado

56 ocorre no nível do individuo para alguns traços chave.

57 Assim, entendendo que a diversidade é um fenômeno complexo,

58 sugerimos levar isso em conta e usar não apenas uma abordagem multi-

59 hipóteses para estudar os seus determinantes, mas também considerar a

60 variação espacial das relações com eles. Para trabalhos futuros com rola-bostas

61 seria interessante entender os eventos históricos e evolutivos que moldaram não

62 só a diversidade, mas que também filtraram diferentes atributos, tanto a nível de

63 espécies, quanto de indivíduos.

64 Palavra-Chave: Macroecologia, Neotrópico, Gradientes Latitudinais de

65 Diversidade, Diversidade Funcional, Variação Intra-específica.

66 ABSTRACT 67

68 The study of diversity patterns has generated many hypotheses, which have been

69 often seen as rivals. However, biodiversity is a complex phenomenon and the

70 result of the effects of multiple drivers acting at the same time, effects that may

71 vary in space. The variance of the complex array of drivers that explain local

72 diversity is important to understand the geographic differences in the effects of

73 land use changes. These drivers act as different filters to the establishment and

74 survival of species populations, changing the composition and structure of the

75 community. They may also filter different individual traits, thus altering the

76 functional structure of the community. We defined three main questions: (1) which

77 are the main drivers of local dung beetle species richness in the Neotropics; (2)

78 whether the relative importance of these drivers varies geographically; (3) and

79 how does the time since land use change affect the functional aspects of the

80 community.

81 For the first question, we constructed a database with published literature

82 on dung beetle communities, to extract information on species richness,

83 abundance, type of bait, type of habitat and sampling effort (as hours/pitfall). We

84 used a multi-hypothesis approach to understand which set of hypotheses better-

85 explained dung beetle species richness at a local scale. Specifically, we used

86 environmental variables to account for six hypotheses: productivity, water–

87 energy, ambient energy, habitat heterogeneity, climatic heterogeneity, and

88 resource heterogeneity, plus a seventh neutral hypothesis described using only

89 spatial data. For the second question, we compiled data from standardized

90 surveys based on pitfall traps, and estimated species richness at each locality

91 using sample coverage estimators. We assessed the relationhips between

92 several predictors (including climate, habitat and mammal diversity) and species

93 richness, and also between them, by means of geographically weighted structural

94 equation mixed models. And for the third question, we conducted standardized

95 surveys of dung beetle communities in seven forest fragments and adjacent

96 pastures at two different regions pertaining to the Atlantic forest (Itajaí Valley) and

97 the Cerrado (Goiânia region) biomes, using pitfall traps baited with human and

98 cow dung, and rotten liver. We measured fourteen traits in individuals collected

99 in each type of habitat at each particular site. And then we calculated the

100 functional richness, functional evenness, functional divergence and community-

101 weighted mean of traits for each area, and analyzed the individual variation

102 through Trait Statistics.

103 We found that Dung Beetle local richness is a result of productivity (by

104 energy and water) and Heterogeneity (both habitat and resource). The analysis

105 of the variables allows to interpret that the “more-individuals hypothesis” is the

106 main mechanism driving dung beetle diversity, through the importance of

107 abundance. This importance is common to all the Neotropics, but the factors that

108 affect abundance vary between regions. Dung beetle diversity presents

109 geographical heterogeneity in the responses to the factors where we can observe

110 three regions: Mesoamerica, Amazonian, and Subtropical South America. Also,

111 Mammal diversity had contributed to dung beetle diversity and abundance

112 differently, mainly as a consequence of the conversion of forest to pastures. The

113 forest–pasture conversion affected dung beetle functional diversity, where the

114 pasture presented lower functional richness in both regions. But the species pool

115 had a greater effect than time for the reduction of the effect of this conversion.

116 The difference in the species pool also reflects in the trait’s individual variance.

117 While in the Atlantic Forest the filtering occurs at the species level, in the Cerrado

118 it occurs at the individual level in some traits.

119 Understanding that biodiversity is a complex phenomenon, we suggest to

120 take this in account and use not only a multi-hypothesis approach to study its

121 drivers, but also to consider the spatial variance of this relations. For future works

122 with dung beetles would be interesting to understand the historical and

123 evolutionary events that not only shape species diversity, but also filter dung

124 beetle traits at the species or individual level.

125 Capítulo 1 126 Keywords: Macroecology, Neotropic, Latitudinal Diversity Gradient, Functional

127 Diversity, Intraspecific variation.

1 Capítulo 1 INTRODUÇÃO GERAL 2

3 Organização da tese 4

5 A diversidade é um fenômeno complexo que é o resultado de um sistema

6 intrincado com múltiplos fatores atuando sinergicamente. Considerando isso,

7 dividimos essa tese em três capítulos. Na primeira, Unveiling the Multiple

8 Drivers of Dung Beetle Local Species Richness in the Neotropics,

9 discutimos a necessidade de compreender as hipóteses LDG como

10 complementares umas às outras e identificamos o conjunto de hipóteses que

11 descrevem melhor a riqueza local de rola-bostas e suas relações. No segundo

12 capítulo, Assessing the Geographical Variations in the Determinants of

13 Dung Beetle Local Species Richness Across the Neotropics, discute-se a

14 variação na importância relativa dos fatores da riqueza local de rola-bostas e

15 compreendemos como as relações desses fatores muda no espaço,

16 identificando quais fatores a importância é mais uniforme e quais fatores têm

17 uma variação mais forte. No terceiro capítulo Forest Conversion Into Pasture

18 Selects Dung Beetle Traits at Different Biological Scales Depending on

19 Species Pool Composition, discutimos a importância do tempo e do pool de

20 espécies na seleção de características de rola-bostas em processos de

21 conversão da floresta, nós também analisamos a seleção de traços com os filtros

22 externos e internos de besouros e identificar qual característica tem maior

23 competição individual ou seleção de habitat, também analisamos como a

24 diversidade funcional responde aos processos de conversão florestal.

25 Considerando isso, organizamos esta introdução em três sessões: Gradiente de

26 Diversidade Latitudinal, Funções Ecológicas e História Natural do Escaravelho.

27 O gradiente de diversidade latitudinal

28 Um dos padrões mais visíveis na ecologia é o gradiente latitudinal de diversidade

29 (LDG). O LDG pode ser descrito como um aumento na riqueza de espécies dos

30 pólos para os trópicos (Pianka, 1966). Essa distribuição desigual da diversidade

31 intrigou os cientistas por muitos anos e é uma das questões mais antigas que os

32 ecologistas procuram resolver (Hawkins, 2001). Nos anos de 1800, Humboldt

33 descreveu esse padrão e propôs a primeira explicação possível para ele: que o

34 aumento da diversidade nos trópicos era devido a variações no clima

35 (temperatura) e resistência ao congelamento (Hawkins, 2001). Desde os dias de

36 Humboldt, a busca por uma explicação aumentou e várias hipóteses foram

37 propostas. Este é um tema tão popular de pesquisa que foi revisto várias vezes

38 (Pianka, 1966; Rohde, 1992; Gaston, 2000; Willi et al., 2003; Mittelbach et al.,

39 2007), e mais de 40 hipóteses foram proposta para explicar tal padrão (Pianka,

40 1966; Hawkins, 2001).

41 As hipóteses que procuram explicar o LDG podem ser classificadas em

42 três grupos, dependendo das variáveis explicativas propostas para explicar as

43 variações geográficas na riqueza de espécies:

44  Hipóteses Espécies – Energia (Hutchinson, 1959) - que assumem que a

45 riqueza varia de acordo com as diferenças na quantidade de energia

46 disponível ou produzida nas regiões tropicais e temperadas (a energia

47 pode ser uma medida direta de produtividade, uma relação de água e

48 temperatura ou simplesmente energia térmica).

49  Hipóteses de Heterogeneidade (Lack, 1969) - que associa riqueza com a

50 variância de variáveis climáticas, de recursos e de habitat; e

51  Hipóteses históricas / evolutivas (Darwin, 1859; Wallace 1878 cf.

52 Mittelbach et al., 2007) - que descrevem o LDG como resultado de

53 processos históricos ou evolutivos (como estabilidade climática, taxa de

54 diversificação, origem evolutiva ou mudanças temporais em taxas de

55 extinção).

56 Dentro das hipóteses espécie-energia, destacam-se três hipóteses

57 específicas: hipótese de produtividade (Wright, 1983), hipótese água-energia

58 (Hawkins et al., 2003) e hipótese de energia ambiente (Turner, 2004). Proposta

59 por Wright (1983), a hipótese da Produtividade, ou simplesmente Hipótese da

60 Energia, é uma derivação da hipótese Espécie-Área e da Teoria do Equilíbrio da

61 Biogeografia Insular (MacArthur & Wilson, 1963). Wright entende a energia como

62 a taxa de produção dos recursos de interesse para o grupo específico de

63 espécies (Wright, 1983). Para as plantas, podemos usar radiação solar ou

64 evapotranspiração real (AET) como um indicador de energia (ver Whittaker &

65 Field, 2000) e, para os animais, podemos usar uma medida direta da quantidade

66 do recurso que eles utilizam. Embora Wright (1983) enfatize que qualquer

67 medida de estimativa de energia pode ser usada, é comum usar a Produtividade

68 Primária Líquida. Uma vez que a energia é pensada como um recurso alimentar,

69 ela é limitada principalmente pela produtividade em uma determinada área, daí

70 porque, após a proposta da hipótese, ela ficou conhecida como a hipótese da

71 Produtividade. A previsão é que localidades com alto consumo de energia

72 hospedarão populações maiores em seu equilíbrio do que lugares com baixo

73 consumo de energia.

74 A Hipótese Água-Energia foi proposta por Hawkins et al. (2003), após

75 O’Brien (1998; 2006) formular a relação biológica para a dinâmica Água-Energia.

76 Ele enfatiza a diferença de diversidade entre regiões tropicais e temperadas

77 como um subproduto das restrições variáveis associadas à energia

78 (temperatura) e água (AET). Onde as entradas de energia são baixas (em altas

79 latitudes), a riqueza é limitada pela energia e em áreas de alta energia (baixa

80 latitude) a riqueza é limitada pela disponibilidade de água (Hawkins et al., 2003).

81 A dinâmica da água-energia apresenta pontos de interrupção onde a interação

82 entre os fatores é perdida, resultando em regiões onde a energia é o principal

83 fator que influencia a riqueza e outra onde a água desempenha o papel mais

84 importante.

85 Ambas as hipóteses abordaram a questão da origem do pensamento LDG

86 em energia na perspectiva da produção e consumo de recursos alimentares,

87 afetando a riqueza em um nível populacional (isto é, diminuindo as taxas de

88 extinção). A hipótese da Energia Ambiental, por outro lado, tem uma explicação

89 mais ecofisiológica, em que a diversidade é o resultado da temperatura

90 diretamente no indivíduo (Turner, Gatehouse, & Corey, 1987). Esta hipótese usa

91 a temperatura como medida da energia ambiental. Em regiões quentes, os

92 ectotérmicos se alimentam e se reproduzem melhor, e isso também acontece

93 com os endotérmicos, já que o consumo de energia para manter o calor corporal

94 é usado para outras funções, como a reprodução (Turner, 2004). Assim, maior

95 diversidade resulta do aumento das taxas de reprodução dadas pela

96 temperatura.

97 Três hipóteses de heterogeneidade podem ser destacadas: A

98 Heterogeneidade Ambiental / ou de Habitat, a Heterogeneidade Sazonal / ou

99 Temporal e a Hipótese da Heterogeneidade de Recursos. A Heterogeneidade

100 Ambiental / Habitat é uma abordagem de teoria de nicho que considera que a

101 maior variação nos habitats permitirá uma maior variação nos fatores ecológicos,

102 permitindo mais formas de explorá-los, aumentando assim a diversidade (Tews

103 et al., 2004). Um exemplo de padrão de acordo com essa hipótese foi mostrado

104 por Lack (1969) ao tentar entender a diversidade de aves em ilhas. Sua

105 conclusão foi que a área e a limitação de dispersão não foram suficientes para

106 explicar o endemismo das aves e a diversidade nas ilhas; em vez disso, a

107 complexidade ambiental foi o principal fator que contribuiu para a diversidade de

108 aves nas ilhas (Lack, 1969). Pianka (1966), em sua revisão, identifica dois

109 aspectos dessa hipótese - o aspecto macro que envolve a topografia como fator

110 gerador de heterogeneidade, e aspectos micro que envolvem atributos locais

111 importantes para o grupo em questão, como copa, características do solo e

112 outros. A heterogeneidade topográfica pode contribuir para uma maior

113 descontinuidade de habitats aumentando o isolamento e promovendo novas

114 adaptações (Simpson, 1964), enquanto as características microambientais

115 podem ajudar a diminuir sobreposições de nicho, aumentando assim a

116 coexistência de espécies (Ricklefs, 1977). A hipótese de Heterogeneidade de

117 Recursos é uma interpretação derivada de recurso da hipótese de

118 Heterogeneidade de Habitat (Ricklefs, 1977). Tem o mesmo pano de fundo, mas

119 assume que a diversidade é aumentada por incrementos no número de

120 diferentes tipos de recursos disponíveis, diminuindo os efeitos da competição

121 (Tilman, 1985).

122 A Heterogeneidade Sazonal / Temporal foi proposta por Sanders (1968)

123 como uma extrapolação da hipótese estabilidade-tempo. Sua interpretação do

124 gradiente latitudinal foi feita analisando comunidades bentônicas e concluiu que

125 regiões com menor sazonalidade apresentaram mais espécies, enquanto

126 regiões com fortes estações marcadas apresentaram comunidades de espécies

127 mais pobres (Sanders, 1968). Este padrão surge como resultado da interrupção

128 do crescimento em estações estressantes.

129 A ideia de que o tempo junto com o clima influenciou a LDG já foi

130 apresentada por Darwin (1859) e Wallace (cf. Mittelbach et al., 2007). As

131 hipóteses que discutem fatores históricos e / ou evolutivos enfocam

132 principalmente a variação no tempo, estabilidade, taxas evolutivas, taxas de

133 extinção e tempo desde a origem do clado (ou seja, o tempo para a

134 diversificação). Três hipóteses principais destacam-se nesta categoria: A

135 Hipótese do Berço, a Hipótese do Museu e o Conservadorismo de nicho. A

136 hipótese do Berço está relacionada com a quantidade de energia, mas afirma

137 que a maior diversidade é o resultado de taxas evolutivas mais altas devido a

138 mais energia (Rohde, 1992). O aumento nos outputs de energia aumentam a

139 mutação molecular e a evolução, além de encurtar o crescimento individual,

140 aumentando o número de gerações por unidade de tempo e, assim, promovendo

141 processos de seleção mais rápidos. Todos esses fatores aumentam as taxas de

142 especiação gerando maior diversidade em regiões de alta energia (Rohde,

143 1992). Em oposição, a hipótese do Museu afirma que a diversidade se acumula

144 como resultado de maior tempo de diversificação, menores taxas de extinção e

145 estabilidade climática (Fischer, 1960; Pianka, 1966). A constância do ambiente

146 favorável nos trópicos diminui as taxas de extinção, permitindo um maior

147 acúmulo de espécies no tempo (Fischer, 1960; Chown et al., 2000).

148 A hipótese do conservadorismo do nicho afirma que, ao longo da

149 evolução, as mudanças de nicho ocorrem lentamente, portanto, as adaptações

150 são geralmente conservadas e as variações fortes no nicho são raras (Peterson,

151 1999). Explica o LDG em três aspectos: as origens da maioria dos clados são

152 tropicais, os eventos de dispersão de regiões tropicais a temperadas são raros

153 e as regiões tropicais tinham áreas maiores em grande parte da história da Terra

154 (Wiens & Donoghue, 2004). De acordo com essa hipótese, as maiores áreas de

155 habitat tropical originaram mais clados e, ao longo da evolução, a conservação

156 de nichos dispersou habitats temperados ou diferentes eventos raros,

157 acumulando maior diversidade nos trópicos ao longo do tempo (Wiens &

158 Graham, 2005).

159 Nesta tese nos concentramos em avaliar os efeitos das hipóteses de

160 energia das espécies e heterogeneidade, principalmente devido à falta de dados

161 filogenéticos bem resolvidos dos besouros rola-bostas neotropicais.

162 Funções ecológicas

163 Desde o início da biologia, tem havido interesse em entender o papel das

164 espécies no meio ambiente, o que elas fazem em ambientes naturais, como sua

165 ação pode afetar outras espécies e o meio ambiente, e como o ambiente pode

166 afetar o que elas fazem. Compreender essas relações não é uma tarefa fácil e

167 envolve interações complexas que podem ajudar a elucidar padrões ecológicos

168 e evolutivos (por exemplo, os tentilhões clássicos de Darwin, Darwin, 1859).

169 Inicialmente, essa questão foi abordada pela classificação de espécies,

170 principalmente pela análise de estruturas morfológicas, ou outras características

171 de espécies como recursos utilizados, interações biológicas e assim por diante,

172 seguidas por avaliações de como a biodiversidade afetava o meio ambiente e

173 como o ambiente afetava a biodiversidade (Weiher et al., 1999). O assunto foi

174 estudado primeiro com plantas como modelos, e foi quase desenvolvido de

175 forma independente em animais (Moretti et al., 2017). No entanto, invertebrados

176 e insetos foram deixados para trás e apenas recentemente foram efetivamente

177 estudados (Moretti et al., 2017). Isto é particularmente digno de nota se

178 considerarmos a quantidade considerável de interações e as importantes

179 funções que os insetos desempenham nos ecossistemas. Entre eles, os

180 escaravelhos são um grupo de besouros bem conhecidos por seu papel

181 ecológico como recicladores da natureza.

182 Na história da ecologia, a funcionalidade foi abordada de várias maneiras

183 sem uma definição adequada do termo “função”. Os primeiros passos para

184 abordar esse tipo de questão foram feitos pela análise da forma das espécies,

185 criando classificações, hoje interpretadas como uma classificação em uma visão

186 funcional, como a proposta por Theophrastus (Weiher et al., 1999). Essa

187 abordagem permanece amplamente utilizada na ecologia e pode ser

188 representada pela extensa classificação de organismos em grupos de acordo

189 com sua forma (formas de vida; Raunkiaer, 1934), exploração de recursos

190 (guildas; Root, 1967) ou uma abordagem integrativa como grupos funcionais

191 (Cummins, 1974). À medida que a descoberta e o conhecimento das espécies

192 aumentavam, tornou-se evidente a necessidade de um estudo além da simples

193 classificação, que visava entender o efeito do ambiente sobre a biodiversidade

194 (por exemplo, a modelagem de características derivadas de forças ecológicas),

195 tentando entender como as características das espécies são afetadas pelo

196 ambiente e por outras espécies (Grime, 1974). O efeito contínuo da antropização

197 (alteração de habitats, perda de espécies e outras) impulsionou outro passo para

198 o campo: os estudos da biodiversidade e das funções do ecossistema. Esta

199 abordagem foi focada na compreensão do efeito da biodiversidade no

200 ecossistema, como o papel da biodiversidade na produção primária (Costanza

201 et al., 2007). Ao longo da história desse campo, podemos visualizar uma

202 mudança constante no foco das respostas ou dos efeitos da biodiversidade no

203 meio ambiente.

204 O aumento de pesquisadores interessados em ecologia funcional

205 culminou em um periódico, editado pela British Ecological Society, focado

206 apenas nestes estudos, Journal of Functional Ecology. Em seus primeiros

207 volumes, houve um cuidado em definir o objetivo do campo e também trouxe

208 uma definição formal de uma função ecológica. Calow (1987) define funções de

209 forma adaptativa, em um processo mantido por seleção. Essa definição ampla e

210 simples criou alguma confusão, então, mais tarde, Jax (2005) revisou o termo e

211 definiu-o de acordo com seus quatro usos: como um processo, como a função

212 (funcionamento) em um sistema, como um papel e como um serviço. Apesar

213 dessas definições, o termo funcionamento permaneceu confuso e, na maioria

214 dos trabalhos, o conceito foi implícito como algo trivial ou uma definição circular,

215 como “função é o que mantém o funcionamento do ecossistema”.

216 Considerando essa falta de definição formal, Nunes-Neto e colaboradores

217 (2014) definiram a função ecológica do ponto de vista hierárquico e

218 organizacional como:

219 “… É um efeito preciso (diferenciado) de determinada ação restringida

220 sobre o fluxo de matéria e energia (processo) realizada por um dado item de

221 biodiversidade em um ecossistema fechado de restrições.” Pg. 9

222 Por item de biodiversidade refere-se a qualquer aspecto da biodiversidade

223 que é o foco das hipóteses estudadas (indivíduos, espécies, guildas) e por

224 fechado de restrições ele significa um modo de dependência de um conjunto de

225 restrições, em que um sistema que produz alguns das restrições que controlam

226 sua dinâmica subjacente realiza o fechamento (Nunes-Neto et al., 2014).

227 Embora esse conceito abarque muitos aspectos funcionais, ele deixou uma

228 característica importante do funcionamento ecológico: os indivíduos não apenas

229 afetam o meio ambiente, mas também respondem às suas variações no meio

230 ambiente, portanto, como o ambiente afeta as funções dos indivíduos? Para isso,

231 devemos perceber a importância da resposta do organismo aos gradientes no

232 ambiente e / ou em outros indivíduos. Diaz et al. (2013) definiram a função de

233 maneira muito mais simples, incorporando o efeito de resposta. Para eles, a

234 função é um ato ou desempenho de um efeito (quando o indivíduo altera o

235 ambiente, outras espécies ou outros indivíduos) ou de uma resposta (quando o

236 indivíduo é afetado pelo ambiente, outras espécies ou outros indivíduos). Assim,

237 o efeito é o resultado de um processo quando o organismo é o sujeito ativo, e a

238 resposta é o resultado de um processo quando o organismo é o sujeito passivo.

239 Outro conceito importante indicado foi o desempenho. Quando o desempenho

240 de uma função é assumido, concordamos que os indivíduos podem variar na

241 forma como eles afetam e respondem ao ambiente, e essa variação afeta

242 diretamente a sua adequação.

243 Função também pode ser definida como um serviço de acordo com Jax

244 (2005), mas é muito importante destacar que isto é somente quando os ganhos

245 humanos (da função) estão sendo levados em consideração. Por exemplo,

246 quando dizemos que a floresta é importante porque reduz o assoreamento em

247 um rio, diminuindo os efeitos das inundações na cidade, estamos identificando o

248 serviço que a floresta está fornecendo para os seres humanos. Também é

249 importante distinguir função de funcionamento; o funcionamento é amplamente

250 aceito como um estado desejável de um sistema, e as espécies são investigadas

251 sobre como elas mantêm o sistema (Huston, 1997).

252 Depois de definir a função, vem um novo desafio: como medi-la? Pode

253 parecer uma tarefa simples, como observar e fazer notações, ou medir

254 diretamente a função. No entanto, na maioria dos casos, há um conhecimento

255 limitado da história natural das espécies e / ou infra-estrutura de pesquisa

256 (orçamento, laboratórios, recursos humanos). Uma maneira comum de resolver

257 este problema e abordar a funcionalidade é usar características das espécies

258 que podem refletir direta ou indiretamente o que elas fazem ou como respondem:

259 isto é, o uso de características funcionais (Calow, 1987). Por um tempo, o

260 atributo foi usado como sinônimo de muitas coisas diferentes. A definição foi

261 estruturada por Violle et al. (Violle et al., 2007) e agora um atributo funcional é

262 definido como qualquer característica mensurável (morfológica, bioquímica,

263 fenológica, fisiológica e comportamental) que é medida no nível individual que

264 afeta sua aptidão. Atributos também podem ser decompostos em efeito e

265 resposta (Violle et al., 2007; Díaz et al., 2013). Estas características são usadas

266 assumindo que são adaptadas à função e interações das espécies(Madewell &

267 Moczek, 2006). Os estudos de atributos emergiram principalmente com plantas,

268 enfatizando traços com relações mais diretas com processos, como a

269 fotossíntese, e o grande número de experimentos permitiu avaliar outros

270 processos como a decomposição (Moretti et al., 2017). O uso de características

271 na ecologia funcional para animais é menos disseminado, embora esteja

272 crescendo, e enfatiza principalmente o estudo dos processos de montagem. Mas

273 é importante destacar a baixa taxa de experimentos em características na

274 ecologia funcional animal (Moretti et al., 2017; Noriega et al., 2018).

275 Escaravelhos, como exceção, são um dos grupos onde alguns experimentos

276 foram realizados (isto é, seleção sexual e com grupos funcionais; Emlen et al.,

277 2005; Slade et al., 2016).

278 Essa abordagem apresentou uma revolução no campo, pois permitiu o

279 trabalho com múltiplas espécies e o estudo de espécies com pouca ou

280 desconhecida informação sobre a história natural. Nos primórdios da ecologia

281 funcional baseada em atributos, os estudos perguntavam como o ambiente

282 afetava os atributos ou como eles afetam o meio ambiente. Mas com o aumento

283 do impacto humano em áreas naturais e extinções, tornou-se mais popular os

284 estudos usando métricas para entender a variação de características nas

285 comunidades e como elas afetam o meio ambiente (McGill et al., 2006). A

286 amplitude e variação de atributos foram usados para medir a parte funcional da

287 biodiversidade, a diversidade funcional.

288 Compreender a história evolutiva dos atributos permitiu analisar a

289 montagem das comunidades e os padrões que promovem a coexistência. Por

290 essa abordagem, as relações entre filogenia e atributos podem explicar os

291 padrões de divergência ou convergência de características. A convergência de

292 características é principalmente influenciada por filtros ambientais, além de

293 alguns filtros bióticos (por exemplo, predação, facilitação); a divergência é

294 influenciada principalmente por filtros bióticos (competição, seleção sexual,

295 deslocamento de caracteres) e heterogeneidade ambiental. Embora, em alguns

296 casos, a diversidade filogenética tenha sido tratada como um proxy para a

297 diversidade de atributos, uma vez que se assumiu que todas as características

298 têm alguma estrutura filogenética, e as filogenias forneceram mais informações.

299 Pavoine & Bonsall (2011) mostraram que, se usarmos a complementaridade de

300 ambos os aspectos da diversidade, poderíamos ter maior avanço na

301 compreensão da montagem da comunidade. A variação de caracteres pode ser

302 decomposta em quatro componentes, a) ambiente de espécies, b) filogenia, c)

303 componente compartilhado de ambiente e filogenia ed) componente inexplicado

304 (Diniz-Filho & Bini, 2008). Cada componente reflete diferentes processos de

305 evolução, um grau mais alto em a representa um forte filtro ambiental, um maior

306 b representa um forte conservadorismo de nicho e um maior c revela uma

307 restrição evolutiva de longa data e uma seleção estabilizadora (Pavoine &

308 Bonsall, 2011).

309 Depois de toda essa discussão e análise de todos os aspectos da função,

310 é seguro definir a função como um efeito de uma ação realizada por um item

311 biológico ou uma resposta a outras condições bióticas e abióticas em um dado

312 sistema. De uma maneira ampla, podemos assumir a função como toda

313 interação que um indivíduo faz durante sua vida, com o ambiente e com outros

314 indivíduos. Isso pode parecer um conceito tão amplo que abrange tudo, mas é

315 muito importante destacar que a função ecológica é muito dependente do

316 contexto. Portanto, há uma necessidade de expressar explicitamente o contexto

317 da função ecológica que você está medindo em seu estudo.

318 História Natural dos Scarabaeinae

319 Escaravelhos são besouros populares, bem conhecidos por suas preferências

320 alimentares por fezes de mamíferos, e seu comportamento peculiar de fazer e

321 rolar bolas de estrume. Este nome é referido aos besouros das subfamílias

322 Scarabaeinae (Latreille, 1802) e Aphodiinae (Leach, 1815) e da família

323 Geotrupidae (Latreille, 1802), que apresenta hábitos coprofágos. O foco principal

324 deste trabalho será nos Scarabaeinae, porque eles são a família mais

325 representativa nas assembléias de besouros florestais no Neotrópico. Os

326 Aphodiinae estão presentes em baixa abundância e os Geotrupidae estão

327 restritos a grandes altitudes, como nos Andes.

328 A subfamília Scarabaeinae inclui cerca de 6500 espécies presentes em

329 quase todos os biomas da Terra (Schoolmeesters, 2019). É importante entender

330 a origem e os padrões evolutivos da família. O registro mais antigo dos

331 Scarabeoidea aparece no jurássico médio, Alloioscarabeus cheni (Bai et al.,

332 2012). Usando dados moleculares, podemos traçar a origem de Scarabaeinae

333 para aproximadamente 118,8-131,6 MA (Gunter et al., 2016). Os clados basais

334 na subfamília eram originalmente saprófagos ou micetófagos, e o

335 comportamento de alimentação de esterco surgiu por volta do Cretáceo médio

336 com a exploração do estrume dos dinossauros e teve uma grande diversificação

337 antes da diversidade dos mamíferos (Gunter et al., 2016). A extinção dos

338 dinossauros afetou os escaravelhos, uma vez que é improvável que as espécies

339 que usavam o esterco dos grandes dinossauros pudessem simplesmente mudar

340 seu hábito alimentar para usar esterco dos pequenos mamíferos insetívoros que

341 coexistiam ao mesmo tempo. Assim, o grupo sobreviveu muito provavelmente

342 graças a pequenas espécies generalistas que exploraram o esterco de pequenos

343 dinossauros e pequenos mamíferos (Gunter et al., 2016).

344 A origem do grupo foi Gondwaniana, aproximadamente no que é hoje a

345 África do Sul (Scholtz et al., 2009). A divisão dos continentes promoveu a

346 primeira irradiação dos escaravelhos, como notaram as tribos basais

347 (Dichotomini e Cantonini). Embora ambas as tribos sejam de origem Afrotropical,

348 atualmente seus representantes podem ser encontrados em quase todas as

349 regiões biogeográficas (Monaghan et al., 2007). O isolamento precoce promoveu

350 uma alta diversificação no grupo, como observado na origem monofilética de

351 tribos e clados exclusivos na América do Sul (Eurysternini, Eucranini, Phaneini,

352 Dichotomius, Canthon e Canthidium) (Monaghan et al., 2007; Gunter et al. ,

353 2016), Australásia (Monaghan et al., 2007) e Madagascar (como o mais recente)

354 (Gunter et al., 2016).

355 Sobreviver comendo esterco exigiu algumas adaptações cruciais que

356 explicam o sucesso desses besouros. Uma adaptação necessária é o aparelho

357 bucal altamente especializado. Diferente da maioria dos besouros que têm

358 partes bucais mastigadoras, os escaravelhos apresentam uma mandíbula

359 adaptada à alimentação mole, com cerdas que atuam como filtros e

360 microestruturas no lobo molar que atuam como um triturador (Hanski &

361 Cambefort, 1991). Uma característica que também mostra adaptação é o

362 sistema reprodutivo feminino, que apresenta apenas um ovariolo ou um ovariolo

363 atrofiado e um grande ovário. Isto resulta em um pequeno número de ovos com

364 alto investimento para lidar com o recurso escasso e heterogêneo que eles usam

365 (Halffter et al., 2013).

366 Outra característica reprodutiva que ajuda a entender seu sucesso é o

367 cuidado parental e, em alguns casos, o comportamento sub-social. A maioria dos

368 cuidados parentais em besouros rola-bostas é pré-oviposição, com a construção

369 não apenas do ninho, mas em alguns casos elaborando bolas de cria que

370 protegem as larvas (Halffter & Matthews, 1966). O comportamento sub-social

371 aparece quando o cuidado parental vai além do ninho, quando os pais

372 permanecem no ninho, protegendo e limpando a ninhada contendo as larvas

373 (Simmons & Ridsdill-Smith, 2011).

374 O olho dos besouros rola-bostas também apresenta algumas

375 modificações em comparação com outros besouros: um prolongamento da gena

376 (ou bochecha) que invade os olhos, e na maioria das vezes os divide. Este

377 prolongamento é chamado de canto e sua função é principalmente para proteger

378 os olhos da abrasão da escavação, e quando divide o olho é responsável por

379 criar diferentes órgãos, um ventral para visão normal e um dorsal para

380 navegação (Scholtz et al., 2009) , extremamente importante nos escaravelhos

381 noturnos, pois eles usam a via láctea para localização à noite (Dacke et al.,

382 2013).

383 Se estudarmos a origem da superfamília Scarabaeoidea, a teoria é que a

384 família começou como um grupo de besouros saprófagos, com maior evolução

385 na coprofagia nas subfamílias acima citadas (Gunter et al., 2016). Toda a

386 superfamília é identificada pela presença de antenas lameladas (Gunter et al.,

387 2016). Esse tipo de antena pode ser outra adaptação para um recurso efêmero,

388 já que a função de antenas lameladas é aumentar a área superficial dos

389 segmentos de antenula, aumentando a recepção olfativa, o que pode ser crucial

390 para encontrar o recurso o mais rápido possível. Embora seja uma hipótese

391 provável, nunca foi testada.

392 Uma característica muito visível nos besouros é a presença e grande

393 variedade de chifres. Até mesmo Darwin (1871) ficou hipnotizado por esse traço

394 e explicou a evolução deles por toda a teoria da seleção sexual. Esta

395 característica é proeminente e mais desenvolvida nos machos, mas em algumas

396 espécies, as fêmeas mostram chifres menores. O principal uso do chifre é lutar

397 ou defender algo (o ninho, a ninhada, a fêmea), o que pode explicar a

398 conspicuidade nos machos, no entanto, outras funções são desconhecidas até

399 agora. O tamanho ou a presença do corno, na maioria dos casos, representa um

400 alto índice de acasalamento bem sucedido (Kotiaho, 2002), mas não se traduz

401 diretamente na qualidade do macho, já que o tamanho e a produção têm altas

402 causas ambientais (Hunt & Simmons, 1997), assim como a produção do chifre é

403 o resultado do número de recursos disponíveis como larvas (Moczek, 1998). A

404 fêmea escolhe um macho com chifres, porque representa um desenvolvimento

405 mais bem-sucedido das larvas, aumentando o tamanho da massa de cria e a

406 defesa do ninho (Hunt & Simmons, 1997), mas representa algum custo para a

407 fêmea, uma vez que ela cria um número menor de massas de crias. Para machos

408 que possuem grandes chifres também há custos envolvidos, considerando que

409 o tempo de desenvolvimento das larvas aumenta (Moczek & Emlen, 2000), a

410 capacidade de manobra nos túneis diminui (Madewell & Moczek, 2006) e a

411 posição do chifre pode diminuir o recurso investido para outras partes do besouro

412 (Pizzo et al., 2012). Chifres de besouro aparecem principalmente em quatro

413 posições, na base, centro ou frente da cabeça e em vários lugares no tórax

414 (Emlen et al., 2005). Dependendo da região onde se desenvolve o chifre existe

415 uma alocação diferenciada de recursos que diminui o investimento em outras

416 estruturas como os olhos (quando emerge no vértice, na área basal da cabeça),

417 antenas (quando emerge na frente ou no clipeo, no centro e na frente da cabeça,

418 respectivamente), asas (quando emerge no meio ou pelos lados do tórax) (Emlen

419 et al., 2005), e em algumas espécies também os testículos (Simmons & Emlen,

420 2006). Mas há algumas condições especiais quando os benefícios superam o

421 custo dos chifres. Por exemplo, os chifres no vértice terão uma vantagem para

422 os besouros diurnos, pois ter olhos menores não representa uma desvantagem

423 contrária aos besouros noturnos (Emlen et al., 2005). Na floresta, onde a alta

424 umidade mantém os produtos químicos no ar por um longo período, uma redução

425 no tamanho das antenas não é uma desvantagem, ao contrário de áreas abertas

426 (Emlen et al., 2005). O custo dos cornos torácicos está relacionado aos eventos

427 de dispersão, já que eles voam para novas massas fecais. Uma característica

428 que pode diminuir as desvantagens de reduzir a manobrabilidade é a densidade

429 da população, pois em uma população de alta densidade a probabilidade de

430 encontros é maior e com aumento da competição sexual o investimento em

431 chifres fornece uma maior taxa de sucesso (Emlen et al., 2005; Buzatto, Tomkins

432 e Simmons, 2012). A relação com chifre e testículos não está presente em todas

433 as espécies. Isto aparece em espécies com machos dimórficos, onde existe uma

434 ampla gama de indivíduos sem chifres até indivíduos com chifres totalmente

435 crescidos (Moczek, 1998). Nesse caso, o investimento em chifres diminui os

436 testículos, mas garante a vantagem reprodutiva ao isolar e proteger a fêmea. O

437 macho sem chifres assegura sua vantagem, agindo como um macho satélite de

438 duas maneiras: a) Ele faz um túnel secundário para encontrar a fêmea, copula

439 com ela e depois foge do ninho, b) simplesmente passa pelo macho maior

440 fingindo ser outra femêa, copula e depois foge (Moczek & Emlen, 2000). Chifres

441 também estão associados ao comportamento de tunelamento, são armas

442 apropriadas para fechar espaços, e até mesmo os besouros que habitam

443 espaços abertos e possuem chifres são derivados de espécies com

444 comportamento de tunelamento (Emlen & Philips, 2006).

445 Uma característica notável dos besouros rola-bosta é o comportamento

446 de rolamento. Esse comportamento evoluiu pelo menos cinco vezes de forma

447 independente no grupo (Gunter et al., 2016). Isso explica a variedade de formas

448 de rolamento (sozinho, feminino no topo macho atrás, macho no topo feminino

449 atrás e outros, Halffter & Matthews, 1966). Com o hábito de rolamento vieram

450 algumas adaptações especialmente nas pernas. As patas dianteiras apresentam

451 a perna típica do besouro, com a presença de tíbia dentada (Crowson, 1981), e

452 as pernas médias apresentam adaptações ao hábito de laminar (Hanski &

453 Cambefort, 1991). Besouros com comportamento de tunelamento tendem a ter

454 tíbias médias menores e mais largas, enquanto besouros com comportamento

455 de rolagem tendem a ter tíbias maiores e mais finas (Vaz-de-Mello, com. Pess.).

456 A extensão das pernas do meio surgiu para manipulação de bola principalmente

457 para dirigir e controlar a ação de rolamento (Gonzalo Halffter & Matthews, 1966).

458 O hábito de rolar apresenta outro trade-off para o traço do escaravelho: o

459 rolamento e a escavação da bola. Hanski & Cambefort (1991) hipotetizaram que

460 a adaptação para uma rolagem mais rápida ocorre se houver uma diminuição na

461 capacidade de escavação, como visto em Neosisyphus spinipes (Thunberg,

462 1818), um escaravelho africano que perdeu completamente a capacidade de

463 escavar e somente esconder a bola de ninhada com folhas e outro conteúdo no

464 ninho(Hanski & Cambefort, 1991).

465 Há mais do que aparenta quando pensamos nas funções do besouro de

466 esterco. A primeira coisa que vem à mente é o óbvio hábito alimentar de esterco

467 que afeta diretamente o ciclo de nutrientes (Halffter & Matthews, 1966).

468 Alimentando-se de esterco, eles não apenas diminuem a quantidade de esterco

469 disponível para moscas, mas também controlam os parasitas, já que os adultos

470 se alimentam de ovos de parasitas nematóides e cestóides, bem como de

471 bactérias (Halffter & Matthews, 1966; Hanski & Cambefort, 1991, Nichols et al.,

472 2008). Da mesma forma, revolvendo e diminuindo o esterco em contato com o

473 ar e a radiação solar, os escaravelhos contribuem para diminuir a emissão de

474 CO2 nas pastagens (Slade et al., 2016). Como a maioria das espécies faz um

475 ninho escavando o solo, outra função é a bioturbação do solo e, ao escavar e

476 enterrar as fezes, aumenta a incorporação de NO3 no solo (Bertone, 2004).

477 Rolando o excremento e enterrando-o, eles atuam como dispersores

478 secundários de sementes e aumentam o crescimento das plantas (Macqueen &

479 Beirne, 1975; Miranda, Santos, & Bianchin, 2000; Andresen, 2002). Alguns

480 besouros que apresentam outros hábitos alimentares fornecem outras funções,

481 como a polinização, observadas em um pequeno número de plantas nas famílias

482 Aracea e Lowiace (Sakai & Inoue, 1999) que emulam o cheiro de esterco para

483 atrair esses besouros. Mais incomum que a polinização é o exemplo de Canthon

484 virens (Mannerheim, 1829), que preda rainhas de Atta spp. regulando a dinâmica

485 populacional dessas espécies (Vasconcelos et al., 2006), e Deltochilum valgun

486 (Burmeister, 1873), que é predador de diplópodos (Larsen et al., 2009).

487 Além disso, importante, principalmente nos Neotrópicos, é o hábito da

488 necrofagia (Halffter & Matthews, 1966), altamente presente na tribo Phanaeini

489 (Edmonds & Zídek, 2010). Este hábito está bem documentado em algumas

490 espécies, embora um pouco negligenciado em questões de função. Assim como

491 os besouros coprófagos, contribuem com a ciclagem de nutrientes e controle de

492 parasitas e moscas, além disso, uma das funções mais icônicas do ponto de

493 vista antropológico é o uso de algumas espécies como evidência forense

494 (Pessôa & Lane, 1941; Almeida et al. , 2015). Outras espécies que apresentam

495 hábitos saprófagos e fungívoros podem participar principalmente na ciclagem de

496 nutrientes.

497 Ao analisar as diferenças de comportamento no uso de recursos, alguns

498 autores agruparam esses besouros nas guildas. A primeira classificação foi

499 proposta de forma evolutiva por Halffter & Matthews (1966) e Halffter & Edmonds

500 (1982). Eles estudaram o comportamento de nidificação de besouros e

501 identificaram quatro grupos. Embora bem detalhado e explicado, esta

502 classificação não foi muito utilizada.

503 As classificações posteriores usaram o comportamento alimentar para

504 abordar os grupos, primeiro proposto por Bornemizsa (1969), ele identificou três

505 grupos principais, Telecoprideos, também chamados de roladores, os besouros

506 que fazem bolas e as rolam para algum lugar distante da fonte de alimento,

507 Paracoprideos , também chamados de tuneleiros ou cavadores, os que enterram

508 a comida diretamente abaixo ou perto da fonte de alimento e os Endocoprideos,

509 também chamados de residentes, os que se alimentam diretamente da fonte.

510 Essa classificação foi então ampliada por Doube (1990) em 7 grupos, utilizando

511 não apenas o comportamento alimentar, mas também o tamanho dos besouros

512 e a velocidade de escavação. Os Telecoprideos foram divididos em 2 grupos

513 usando o tamanho para agrupá-los em telecoprideos grandes e pequenos. Os

514 Paracoprideos foram divididos em 3 grupos usando tamanho e velocidade para

515 classificá-los em Paracoprideos Grandes Rápidos, Paracoprideos Grandes

516 Lentos e Paracoprideos Pequenos. Os Endocopridos permaneceram intactos, e

517 ele identificou um novo grupo chamado Cleptocoprideos, besouros que exploram

518 ninho ou bolas de outras espécies. Embora largamente usada e citada (128

519 vezes - Google Scholar), essa classificação nem sempre foi totalmente utilizada,

520 principalmente porque a caracterização para rápido e lento não é definida no

521 artigo e, às vezes, provavelmente para uma classificação mais rápida sem a

522 necessidade de medir os besouros.

523 A desvantagem da classificação é que na maioria dos casos sabemos

524 apenas a identidade da espécie. Com pouca informação biológica vêm

525 generalizações, que podem ser perigosas e excluir informações sobre a

526 funcionalidade das espécies. O uso de traços pode resolver esse problema.

527 Usando um grupo de caracteres que podem ser medidos diretamente no

528 indivíduo, diminuímos o problema de espécies com biologia desconhecida e

529 podemos ter uma melhor compreensão de sua funcionalidade.

530 Atributos Funcionais de Scarabaeinae

531 Assim, descrevemos as características do escaravelho relacionadas à sua

532 funcionalidade; seus atributos. Para uma melhor compreensão, organizei essa

533 seção pelo tipo de traço (morfológico, comportamental, fenológico e fisiológico)

534 e focalizei principalmente os adultos, pois trabalhos com larvas são raros. As

535 características morfológicas são assumidas aqui como qualquer característica

536 envolvendo medidas diretamente obtidas no corpo ou estruturas individuais.

537 Comportamental é qualquer característica que envolve algum tipo de

538 comportamento ou estratégia adotada pelo indivíduo. Fenológico é qualquer

539 atributo relativo a tempo. Por fim, características fisiológicas são atributos

540 envolvidos com taxas metabólicas e fisiológicas.

541 Morfológico:

542 Tamanho: Esta característica é comumente usada em estudos ecológicos, por

543 causa do fácil acesso e por causa da quantidade de informação dada. É

544 comum usar experimentos de exclusão para entender o efeito e a resposta

545 do atributo (g.e. Slade et al., 2016). Esta característica pode variar em

546 função do tipo de solo (solos argilosos excluem espécies pequenas),

547 perturbações (espécies maiores são mais sensíveis à perturbação do

548 habitat) e precipitação (espécies maiores aparecem quando a estação

549 chuvosa começa).

550 Medição: Medida pela pesagem direta do besouro (massa); o produto do

551 comprimento, altura e largura do corpo (volume); ou pela medida linear do

552 comprimento total do besouro (comprimento). O comprimento também

553 pode ser medido pela soma do comprimento do pronoto e do comprimento

554 dos élitros, porque é comum haver variações na posição da cabeça quando

555 o indivíduo está fixado.

556 Efeito: pode refletir o custo de desenvolvimento, a quantidade de recurso

557 utilizado, vantagens competitivas, sucesso de acasalamento, quantidade

558 de dispersão secundária de sementes, tamanho máximo de dispersão de

559 sementes, capacidade de solos compactados rotativos, substitutos à força.

560 Eu assumo que massa corporal, volume e comprimento fornecem a mesma

561 informação biológica por causa da alta correlação entre estes atributos

562 (Radtke & Williamson, 2005).

563 Área / tamanho de Protibia: Analogamente à área de uma pá. A perna da frente

564 é usada principalmente para escavar não apenas o solo, mas também as

565 fezes.

566 Medição: Comprimento ou área total da estrutura.

567 Efeito: pode refletir indiretamente na velocidade da escavação. Esta

568 característica não tem sido amplamente utilizada, portanto, a relação é

569 teórica.

570 Altura do protórax: Este é outro traço envolvido na escavação. Os músculos da

571 perna da frente estão inseridos nessa região, e a altura do protórax reflete

572 diretamente no tamanho da fibra muscular usada para mover a perna.

573 Medição: Esta característica é medida a partir da base da inserção da perna

574 (coxa) até a parte mais alta do pronoto, excluindo chifres.

575 Efeito: fornece uma medida indireta da força da perna e pode refletir a

576 capacidade de escavação em gradientes de dureza do solo.

577 Razão da mesotibia: Esta relação reflete um padrão morfológico para a

578 identificação de roladores e cavadores. Os roladores têm pernas

579 intermediárias finas e alongadas e os cavadores têm pernas curtas e largas

580 (Vaz-de-Mello pers. Com.). Esta relação foi testada por Vaz-de-Mello em

581 uma obra durante sua tese, mas nunca foi publicada.

582 Medição: largura apical da mesotíbia dividida pelo comprimento total da

583 mesotíbia.

584 Efeito: pode refletir a vantagem competitiva para isolar o recurso para

585 alimentação ou acasalamento.

586 Comprimento da metatíbia: A última perna do escaravelho está relacionada ao

587 comportamento de rolamento. Ele serve como um controle de direção e é

588 usado para dar velocidade à bola (Gonzalo Halffter & Matthews, 1966;

589 Hanski & Cambefort, 1991). Além de descrições observacionais, não há

590 dados experimentais.

591 Medição: comprimento total da metatíbia.

592 Efeito: Este traço está relacionado com a velocidade de rolamento.

593 Carga alar: Assume-se que com uma carga de asa maior o indivíduo terá uma

594 melhor sustentação ao voar aumentando a taxa de dispersão.

595 Medição: a relação entre a área da asa e o tamanho ou massa corporal. Há

596 pouca experimentação sobre esse traço (g.e. Larsen, Lopera, & Forsyth,

597 2008)

598 Efeito: fornece uma medida indireta da capacidade de dispersão do

599 besouro.

600 Número de sensilas por µm2: o escaravelho usa o olfato para encontrar massas

601 fecais e parceiros de acasalamento. Então, a antena é uma estrutura

602 preciosa e fornece uma característica que corresponde à capacidade

603 olfativa do indivíduo. Medir diretamente a antena pode não inferir uma

604 sensação de capacidade olfativa porque a estrutura principal na antena, a

605 sensilla, pode variar em densidade em cada espécie, mesmo em espécies

606 com grandes segmentos de antena (Inouchi et al., 1987; Kim & Leal, 2000).

607 Assim, a melhor maneira de garantir a medida da capacidade olfativa é

608 medir o número de sensilas por µm2 ou a densidade da sensibilidade.

609 Medição: por microscopia eletrônica, selecionando uma pequena área e

610 contando o número de sensilas por área.

611 Efeito: reflete uma medida de acuidade olfativa, em um sentido amplo,

612 mede a capacidade de localizar o recurso que utiliza e essa informação é

613 usada para navegar até chegar ao recurso.

614 Área dos olhos: Embora o principal sentido para encontrar companheiros e

615 recursos seja o olfato, os olhos são muito importantes para o escaravelho,

616 navegação, prevenção de obstáculos e orientação ao voar e andar (Byrne

617 & Dacke, 2011). Existe alguma experimentação isolada com esta

618 característica (por exemplo, Dacke et al., 2013).

619 Medição: Esta característica pode ser medida por imagens da cabeça que

620 toma a área do olho ventral e dorsal, ou com a dissecção completa do olho

621 para uma medida da área total.

622 Efeito: Ao avaliar a área dos olhos, podemos medir indiretamente a

623 capacidade de navegação e orientação.

624 Área / largura do clípeo (Figura 1.2.i): Como os escaravelhos usam a cabeça

625 para ajudar no processo de escavação, o clípeo é uma boa estrutura para

626 inferir uma capacidade de escavação. É usado principalmente como

627 alavanca para destacar algumas partículas do solo e a bola das fezes. No

628 caso especial de Canthon virens, também é usado como uma alavanca

629 para decapitar a rainha Atta. Esta característica apresenta apenas estudos

630 observacionais.

631 Medição: É medida pela área total ou pela largura máxima da estrutura.

632 Efeito: Este atributo apresenta mais informações sobre a capacidade de

633 escavação.

634 Chifres: Como explicado antes, chifres são usados principalmente para

635 competição de machos, ou em raros casos de fêmeas que disputam

636 alimentos. Em trabalhos funcionais, esse traço nunca foi usado.

637 Medição: Para um nível comunitário, aconselho usar esta característica

638 como presença ou ausência, uma vez que nem todas as espécies

639 apresentam chifres. Se usarmos as relações de trade-offs mostradas

640 acima, podemos usar também a localização do chifre como uma

641 característica.

642 Efeito: Sua presença indica disputa por acasalamento e disputa por

643 recursos. Pode refletir não só uma forte seleção sexual por sua presença,

644 mas a variação de seu tamanho pode ser usada como um indicador indireto

645 do sucesso de acasalamento (somente intra populacional).

646 Resposta: A localização do corno revela algumas restrições e alguns

647 aspectos ecológicos onde o chifre é mais vantajoso como explicado acima.

648 Boca: o escaravelho se alimenta de pequenas partículas de fezes, contendo

649 bactérias, células epiteliais do intestino de mamíferos, parasitas de ovos, e

650 exclui partículas larvas filtrando estruturas da mandíbula.

651 Medição: Podemos medir isso em dois aspectos, a área das partes duras

652 da mandíbula usada para filtrar, ou medir com experimentos usando

653 partículas com tamanho conhecido (g.e. Holter et al., 2002).

654 Efeito: Esta característica indica o tamanho da partícula ingerida por um

655 escaravelho, e correlaciona com os possíveis tamanhos de ovos de

656 parasitas que pode ser destruído.

657 Cor: A coloração no escaravelho não está envolvida com toxicidade,

658 principalmente com termorregulação e criptização com o solo. A gama de

659 cores varia entre o preto e os indivíduos coloridos muito iridescentes. E

660 algumas relações dessa diferença dão suporte ao uso dessa característica

661 como medida indireta da atividade diária. Hernández (2002) mostrou ser

662 seu estudo que existe uma alta correlação entre a atividade diária,

663 mostrando que as espécies diurnas são mais propensas a serem coloridas

664 e as noturnas com maior probabilidade de serem negras.

665 Medição: Este atributo é normalmente usado de forma binomial como

666 colorido ou não. Existem alguns estudos sobre este assunto (g.e. Vulinec,

667 1997; Hernández, 2002).

668 Resposta: A coloração é uma resposta à predação em áreas abertas e a

669 variações de temperatura, uma vez que em espécies que utilizam habitats

670 abertos e florestais, os indivíduos presentes em áreas abertas apresentam

671 cores mais iridescentes (Hernández pers. Com.).

672 Comportamental:

673 Comportamento de Realocação: Esta é a característica mais usada na pesquisa

674 de Scarabaeinae. Compreende as três principais guildas de roladores,

675 cavadores e residentes (Doube, 1990), dado um aspecto funcional rápido

676 das comunidades. Em alguns casos, esse traço é descrito como um tipo de

677 aninhamento, mas eu assumo o aninhamento como um comportamento

678 com um propósito puro de acasalamento, e este comportamento também é

679 usado para alimentação. Esta característica é muito utilizada e tem sido

680 experimentada com bioensaios de exclusão, usando algum tipo de parede

681 para excluir roldores, um quadrado de plástico embaixo do estrume para

682 excluir cavadores (g.e. Slade et al., 2007; Braga et al., 2013).

683 Medição: esta característica é inferida categoricamente por estudos

684 observacionais ou por generalizações.

685 Efeito: pode refletir a vantagem competitiva para isolar o recurso para

686 alimentação ou acasalamento.

687 Aninhamento: Esta característica descreve os hábitos de nidificação dos

688 besouros rola-bostas. Embora tenha alguns estudos observacionais, ele

689 não foi explorado experimentalmente.

690 Medição: Este traço pode ser descrito de duas formas, de forma binária, se

691 faz ou não aninhamento, ou de forma mais complexa, descrevendo

692 algumas categorias mais derivadas de nidificação, como algumas espécies

693 que fazem uma câmara de solo esférico ou em forma de pêra. em torno da

694 bola de ninhada para proteger as larvas (Halffter & Matthews, 1966).

695 Efeito: Isso reflete o investimento na reprodução ou no cuidado larval.

696 Número de ovos: A maioria das espécies coloca apenas um ovo em uma bola

697 de cria. Mas algumas espécies dividem a bola em menor quantidade com

698 mais ovos. Este traço não é usado frequentemente devido à necessidade

699 de reprodução dos besouros em laboratórios.

700 Medição: Esta característica é comumente medida em laboratório, criando

701 os besouros e acessando o ninho. Para algumas espécies é possível fazer

702 revisões de literatura.

703 Efeito: Uma medida indireta de investimento de desenvolvimento larval.

704 Resposta: Esta característica pode variar com a quantidade e a qualidade

705 do recurso.

706 Profundidade de excavação: Muito importante para os cavadores, uma vez que

707 eles competem diretamente pelo espaço abaixo da massa fecal.

708 Medição: Mede-se por escavação cuidadosa dos túneis ou por estudos no

709 laboratório.

710 Efeito: A profundidade do enterro pode indicar a quantidade de bioturbação

711 fornecida pelo escaravelho.

712 Resposta: Também indica quais espécies competem mais diretamente e

713 uma resposta à dureza do solo.

714 Dieta: Esta característica pode refletir a preferência em determinado recurso

715 alimentar.

716 Medição: Esta característica é usada principalmente categoricamente, ou

717 por alguma experimentação no campo ou por revisão de literatura. Eu

718 aconselho a usar este traço de forma contínua, porque é fácil fazer

719 experimentos com diferentes tipos de esterco e outros recursos. Dessa

720 forma, ele pode ser medido por proporção direta ou usando índices de

721 amplitude de nicho, como o índice de Levin ou o IndVal.

722 Efeito: reflete quanto do recurso é utilizado pelo besouro em larga escala e

723 afeta a ciclagem de nutrientes.

724 Dieta larval: Em alguns casos, o adulto se alimenta em um tipo de recurso e

725 fornece outro tipo para as larvas. Isso acontece principalmente em alguns

726 besouros necrófagos e alguns fungos (Hanski & Cambefort, 1991). Esse

727 traço não é muito usado porque envolve as larvas.

728 Medição: pode ser medido binário simplesmente abordando a semelhança

729 entre dieta adulta e larval, ou categoricamente especificando o tipo de dieta.

730 Efeito: É significativo para abordar essa característica, pois amplifica a

731 gama de nutrientes ciclados pelo indivíduo / espécie.

732 Preferência de habitat: Há uma mudança na composição da comunidade, por

733 exemplo, áreas abertas e florestais. Não apenas por causa da história

734 evolutiva, mas também por alguns custos ecológicos e trade-offs (Krell et

735 al., 2003).

736 Medição: Esta característica é usada principalmente categoricamente, ou

737 por revisão de literatura. Eu aconselho a usar esse atributo de forma

738 contínua, porque há um baixo custo na coleta desses besouros e eles

739 respondem rapidamente. Dessa forma, ele pode ser medido por proporção

740 direta ou usando índices de amplitude de nicho, como o índice de Levin ou

741 o IndVal.

742 Efeito: Esta característica pode ser interpretada como o quanto as espécies

743 contribuem para a função desse habitat ou o número de habitats que uma

744 espécie pode ocupar e executar.

745 Termorregulação: Este traço envolve algumas estratégias que os besouros

746 utilizam para ativamente termorregular. O mais comum é o atrito, ou alta

747 ativação da asa sem a abertura do elitro, ou durante o voo (JR Verdú,

748 Arellano, & Numa, 2006), mas existem outras estratégias como o uso de

749 diferentes horas do dia ou usando a bola de esterco como refúgio térmico

750 (Smolka et al., 2012). Esta característica é descrita apenas para algumas

751 espécies.

752 Medição: Esta característica pode ser abordada categoricamente (por qual

753 tipo de estratégia usada) ou pode ser medida com experimentos e câmeras

754 térmicas.

755 Fisiológico: 756 Temperatura de atividade: Esta característica indica a energia mínima para a

757 atividade do besouro. Não é usado com frequência.

758 Medição: É medida em laboratórios ou por modelos feitos através de

759 experimentos de campo.

760 Resposta: esta característica representa a temperatura mínima para o

761 indivíduo começar a executar suas funções.

762 Tipo respiratório: o tipo de respiração nos insetos pode refletir a quantidade de

763 tempo que um indivíduo pode tolerar o ambiente com pouco oxigênio e

764 também refletir a perda de água (Chown & Davis, 2003).

765 Medição: Esta característica é medida em laboratório e não é usada com

766 frequência. Ele é abordado categoricamente com três tipos, contínuos,

767 cíclicos ou descontínuos.

768 Efeito: reflete a quantidade de tempo que o besouro pode permanecer

769 enterrado no esterco e a taxa de trocas gasosas que podem resultar em

770 economia na perda de água.

771 Resposta: Esta característica é uma resposta a altas temperaturas e a

772 inundações.

773 Tolerância térmica: A tolerância térmica indica a temperatura máxima e mínima

774 que o besouro permanece vivo. Esta característica não é usada

775 regularmente em estudos funcionais de escaravelhos.

776 Medição: É medida em experimentos em laboratório, avaliando as

777 temperaturas mínima e máxima onde metade da população morre.

778 Efeito: Este traço reflete toda a gama de habitats onde o besouro poderá

779 viver (Verdú & Lobo, 2008).

780 Resposta: Esta característica é uma resposta à variação térmica nos

781 habitats.

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1095

1096 Capítulo 2 GENERAL INTRODUCTION 1097

1098 Thesis organization

1099 Diversity is a complex phenomenon which is the result of a intricated system with

1100 multiple factors acting synergistically. Considering this, we divided this thesis in

1101 three chapters. In the first, Unveiling the Multiple Drivers of Dung Beetle Local

1102 Species Richness in the Neotropics, we discuss the need to understand the

1103 LDG hypothesis as complementary to each other and try to identify the set of

1104 hypotheses that better describe dung beetle local richness and their relations. In

1105 the second chapter, Assessing the Geographical Variations in the

1106 Determinants of Dung Beetle Local Species Richness Across the

1107 Neotropics, we discuss the variance in the relative importance of drivers of dung

1108 beetle local richness and try to understand how the driver’s relations change in

1109 space, identifying which factors importance are more constant and which factors

1110 have a stronger fluctuation. In the third chapter, Forest Conversion Into

1111 Pasture Selects Dung Beetle Traits at Different Biological Scales

1112 Depending on Species Pool Composition, we discuss the importance of time

1113 and species pool in the selection of dung beetle traits in forest conversion

1114 processes, we also analyze external and internal filtering in dung beetle traits

1115 selection and identify which trait has a greater individual competition or habitat

1116 selection, also we analyzes how functional diversity respond to forest conversion

1117 processes. Considering this, we organized this introduction in three sessions:

1118 Latitudinal Diversity Gradient, Ecological Functions and Dung Beetle Natural

1119 History.

1120 The Latitudinal Diversity Gradient 1121 One of the most conspicuous patterns in ecology is the latitudinal diversity

1122 gradient (LDG). The LDG can be described as an increase in the species richness

1123 from the poles towards the tropics (Pianka, 1966). This uneven distribution of

1124 diversity has intrigued scientists for many years and is one of the oldest questions

1125 seek to be solved by ecologists (Hawkins, 2001). In the 1800s Humboldt

1126 described this pattern and proposed the first possible explanation for it: that the

1127 diversity increase in the tropics was due to variations in climate (temperature)

1128 and freezing resistance (Hawkins, 2001). Since the days of Humboldt, the search

1129 for an explanation has increased and a multitude of hypothesis have been

1130 proposed. This is such a popular theme of research that it has been reviewed

1131 several times (Pianka, 1966; Rohde, 1992; Gaston, 2000; Willi et al., 2003;

1132 Mittelbach et al., 2007), and more than 40 hypotheses have been proposed to

1133 explain such pattern (Pianka, 1966; Hawkins, 2001).

1134 The hypotheses seeking to explain the LDG can be classified in three

1135 groups depending on the explanatory variables proposed to account for the

1136 geographical variations in species richness:

1137  Species–Energy Hypotheses (Hutchinson, 1959)– that assume that richness

1138 varies according to the differences in the amount of energy available or

1139 produced in the tropical and temperated regions (energy may be a direct

1140 measure of productivity, a ratio of water and temperature, or simply thermal

1141 energy).

1142  Heterogeneity Hypotheses (Lack, 1969)– that associate richness with the

1143 variance in climatic, resources, and habitat variables; and

1144  Historical/evolutionary Hypotheses (Darwin, 1859; Wallace 1878 cf.

1145 (Mittelbach et al., 2007)) –that describe the LDG as the result of historical or

1146 evolutionary processes (such as climatic stability, diversification rate,

1147 evolutionary origin or temporal changes in extinction rates).

1148 Within the species–energy hypotheses, we highlight tree specific

1149 hypotheses: Productivity hypothesis (Wright, 1983), Water–energy hypothesis

1150 (Hawkins et al., 2003) and Ambient Energy Hypothesis (Turner, 2004). Proposed

1151 by Wright (1983), the Productivity hypothesis, or simply Energy hypothesis, is a

1152 derivation of the Species–Area hypothesis and the equilibrium theory of island

1153 biogeography (MacArthur & Wilson, 1963). Wright understands energy as the

1154 rate of production of the resources of interest for the particular species group

1155 (Wright, 1983). For plants, we may use solar radiation or actual

1156 evapotranspiration (AET) as an energy indicator (see Whittaker & Field, 2000),

1157 and for animals we may use a direct measure of the quantity of the resource that

1158 they utilize. Although Wright (1983) emphasizes that any energy estimation

1159 measure can be used, it is common to use Net Primary Productivity. Since energy

1160 is thought as a feeding resource, it is primarily limited by productivity in a given

1161 area, hence why after the proposal of the hypothesis it became known as the

1162 Productivity hypothesis. The prediction is that localities with high energy input will

1163 host larger populations at their equilibrium than places with low energy input.

1164 The Water–Energy Hypothesis was proposed by Hawkins et al. (2003),

1165 after O’Brien (1998; 2006) formulation of the biological relativity to Water–Energy

1166 dynamics. It emphasizes the diversity difference between tropical and temperate

1167 regions as a by-product of the varying constrains associated with energy

1168 (temperature) and water (AET). Where energy inputs are low (in high latitudes),

1169 richness is constrained by energy and in high energy areas (low latitudes)

1170 richness is constrained by water availability (Hawkins et al., 2003). The water–

1171 energy dynamics presents breakpoints where the interaction between the factors

1172 is lost, resulting in regions where energy is the main factor influencing richness

1173 and other where water plays the most important role.

1174 Both hypotheses addressed the question of the origin of the LDG thinking

1175 in energy from the perspective of the production and consumption of feeding

1176 resources, affecting richness in a population level (i.e. diminishing extinction

1177 rates). The Ambient Energy hypothesis, on the other hand, has a more

1178 ecophysiological explanation, where diversity is the result of temperature directly

1179 on the individual (Turner, Gatehouse, & Corey, 1987). This hypothesis uses

1180 temperature as the measure of ambient energy. In warm regions ectotherms feed

1181 and reproduce better, and this also happens to endotherms, since energy

1182 consumption for maintaining body heat is used for other functions, like

1183 reproduction (Turner, 2004). So, higher diversity results from increased

1184 reproduction rates given by temperature.

1185 Three Heterogeneity hypotheses can be highlighted: The Ambient/Habitat

1186 Heterogeneity, the Seasonal/Temporal Heterogeneity, and Resource

1187 Heterogeneity Hypothesis. The Ambient/Habitat Heterogeneity is a niche theory

1188 approach that considers that higher variation in habitats will allow a greater

1189 variance in ecological factors permitting more ways to exploit them, thus

1190 increasing diversity (Tews et al., 2004). One example of pattern in agreement

1191 with this hypothesis was shown by Lack (1969) when trying to understand bird

1192 diversity in islands. His conclusion was that area and dispersal limitation were not

1193 enough to explain bird endemism and diversity in islands; rather, environmental

1194 complexity was the main factor contributing to bird diversity in islands (Lack,

1195 1969). Pianka (1966), in his review, identifies two aspects of this hypothesis – the

1196 macro aspect involving topography as the factor generating heterogeneity, and

1197 micro aspects involving local attributes important for the group in question, such

1198 as canopy, soil characteristics and others. Topographic heterogeneity may

1199 contribute to a larger discontinuity of habitats increasing isolation and fostering

1200 new adaptations (Simpson, 1964), while the microenvironmental characteristics

1201 may help to diminish niche overlaps, thus increasing species coexistence

1202 (Ricklefs, 1977). The Resource Heterogeneity hypothesis is a resource derived

1203 interpretation of the Habitat Heterogeneity hypothesis (Ricklefs, 1977). It has the

1204 same background but assumes that diversity is increased by increments in the

1205 number of different types of resources available, by diminishing competition

1206 effects (Tilman, 1985).

1207 The Seasonal/Temporal Heterogeneity was proposed by Sanders (1968)

1208 as an extrapolation of the stability–time hypothesis. His interpretation of the

1209 latitudinal gradient was made analyzing benthic communities and concluded that

1210 regions with lesser seasonality presented more species, while regions with strong

1211 marked seasons presented poorer species communities (Sanders, 1968). This

1212 pattern arises as a result of the interruption of growing in stressful seasons.

1213 The idea that time along with climate influenced the LDG was already

1214 presented by Darwin (1859) and Wallace (cf. Mittelbach et al., 2007). The

1215 hypotheses discussing historical and/or evolutionary factors mostly focus in the

1216 variance in time, stability, evolutionary rates, extinction rates and time since the

1217 origin of the clade (i.e. time for diversification). Three main hypotheses outstand

1218 in this category: The Cradle Hypothesis, the Museum Hypothesis and niche

1219 conservatism. The Cradle hypothesis is related to the amount of energy, but

1220 states that higher diversity is the result of higher evolutionary rates due to more

1221 energy (Rohde, 1992). The increase in energy outputs increases molecular

1222 mutation and evolution, and also shortens individual growth, increasing the

1223 number of generations per unit of time, and thus promoting more rapid selection

1224 processes. All these factors increase speciation rates generating higher diversity

1225 in high-energy regions (Rohde, 1992). In opposition, the Museum hypothesis

1226 states that diversity accumulates as an outcome of greater time for diversification,

1227 lower extinctions rates and climatic stability (Fischer, 1960; Pianka, 1966). The

1228 constancy of favorable environment in the tropics diminishes extinction rates

1229 permitting a greater accumulation of species in time (Fischer, 1960; Chown et al.,

1230 2000).

1231 The Niche Conservatism hypothesis states that throughout evolution,

1232 niche changes occur slowly, thus adaptations are generally conserved and strong

1233 variations in niche are rare (Peterson, 1999). It explains the LDG in three

1234 aspects: the origins of most clades are tropical, dispersion events from tropical to

1235 temperate regions are rare, and tropical regions had larger areas in throughout a

1236 large part of Earth’s history (Wiens & Donoghue, 2004). According to this

1237 hypothesis the larger areas of tropical habitat originated more clades, and

1238 throughout evolution niche conservation made dispersal to temperate or different

1239 habitats rare events, accumulating greater diversity in the tropics along time

1240 (Wiens & Graham, 2005).

1241 In this thesis we focused in assessing the effects of species–energy and

1242 heterogeneity hypotheses, mostly due to the lack of well-resolved phylogenetic

1243 data on Neotropical dung beetles.

1244

1245 Ecological Functions

1246 Since the beginning of biology, there has been interest in understanding the role

1247 of species on the environment, what they do in natural environments, how their

1248 doing may affect others species and the environment, and how the environment

1249 may affect what they do. Understand these relationships is not an easy task, and

1250 involves complex interactions that may help elucidate ecological and evolutionary

1251 patterns (e.g. the classical Darwin’s finches, Darwin, 1859). Initially, this issue

1252 was addressed by classifying species, mostly by analyzing morphological

1253 structures, or other characteristics of species like resource utilized, biological

1254 interactions and so on, followed by assessments of how biodiversity affected the

1255 environment, and then how the environment affected biodiversity (Weiher et al.,

1256 1999). The subject was studied first with plants as models, and was almost

1257 developed independently in animals (Moretti et al., 2017). However, invertebrates

1258 and insects were left behind and just recently are been effectively studied (Moretti

1259 et al., 2017). This is particularly noteworthy if we consider the sizeable amount of

1260 interactions and the important functions that insects perform in the ecosystems.

1261 Among them, dung beetles are a group of beetles well known for their ecological

1262 role as recyclers of nature.

1263 In the history of ecology, functionality was addressed in several ways

1264 without a proper definition of the term “function”. The first steps to address this

1265 kind of questions were made by the analysis of the form of species, creating

1266 classifications, today interpreted as a classification in a functional view, like the

1267 one proposed by Theophrastus (Weiher et al., 1999). This approach remains

1268 widely used in ecology and can be represented by the extensive classification of

1269 organisms in groups according to their form (life-forms; Raunkiaer, 1934),

1270 resource exploitation (guilds; Root, 1967), or an integrative approach like

1271 functional groups (Cummins, 1974). As the discovery and knowledge of species

1272 was increasing, it became evident the need for a study beyond the simple

1273 classification, which aimed to understand the effect of the environment on

1274 biodiversity (e.g. as the shaping of traits derived from ecological forces), trying to

1275 understand how the characteristics of species are affected by the environment

1276 and by other species (Grime, 1974). The continuous effect of anthropization

1277 (altering habitats, losing species and others) supposed another step for the field:

1278 the studies of biodiversity and ecosystem functions. This approach was focused

1279 on understanding the effect of biodiversity in the ecosystem, such as the role of

1280 biodiversity in primary production (Costanza et al., 2007). Throughout the history

1281 of this field, we can visualize a constant shift in the focus on either the responses

1282 or the effects of biodiversity on the environment.

1283 The increase of researchers interested in functional ecology culminated in

1284 a journal, edited by the British Ecological Society, focused solely in these studies,

1285 Journal of Functional Ecology. In its first volumes, there was a care to define the

1286 objective of the field and also brought a formal definition of an ecological function.

1287 Calow (1987) defines functions in an adaptive way, in a process maintained by

1288 selection. This broad and simple definition created some confusion, so later, Jax

1289 (2005) reviewed the term and defined it according to its four uses: as a process,

1290 as the function(ing) in a system, as a role and as a service. Despite these

1291 definitions, the term functioning remained confusing, and in most of the works the

1292 concept was implicit as something trivial or a circular definition was provided,

1293 such as “function is what maintains the ecosystem functioning”.

1294 Considering this lack of a formal definition, Nunes-Neto et al. (2014)

1295 defined ecological function from a hierarchical, organizational point of view as:

1296 “… is a precise (differentiated) effect of a given constraining action on the

1297 flow of matter and energy (process) performed by a given item of biodiversity in

1298 an ecosystem of closure of constraints.” Pg. 9.

1299 By item of biodiversity it refers to any biodiversity aspect that is the focus

1300 of the studied hypotheses (individuals, species, guild), and by closure of

1301 constraints he means a mode of dependence of a set of constraints, in which a

1302 system that produces some of the constraints harnessing its underlying dynamics

1303 realizes closure (Nunes-Neto et al., 2014). Although this concept embraces a lot

1304 of functional aspects, it left behind an important characteristic of ecological

1305 functioning: individuals not only affect the environment but also respond to his

1306 variations in the environment, so how does the environment affect the functions

1307 of individuals? For this, we must realize the importance of the response of the

1308 organism to gradients in the environment and/or others individuals. Diaz et al.

1309 (2013) defined function in a much simpler way, yet incorporating the response

1310 effect. For them, function is an act or performance of an effect (when the

1311 individual alters the environment, other species or other individuals) or of a

1312 response (when the individual is affected by the environment, other species or

1313 other individuals). So, the effect is the result of a process when the organism is

1314 the active subject, and the response is the result of a process when the organism

1315 is the passive subject. Another important concept indicated was the performance.

1316 When the performance of a function is assumed, we agree that individuals may

1317 vary in how they affect and respond to the environment and this variation directly

1318 affects their fitness.

1319 Function can also be defined as a service according to Jax (2005), but it

1320 is very important to highlight that this is only when human gains (from the

1321 function) is taking into consideration. For instance, when we say that the forest is

1322 important because it reduces siltation in a river, lessening the effects of floods in

1323 the city, we are identifying the service that the forest is providing for humans. It is

1324 also important to distinguish function from functioning; functioning is broadly

1325 assumed as a desirable state of a system, and the species are investigated as

1326 for how they maintain the system (Huston, 1997).

1327 After defining function, comes a new challenge: how to measure it? It may

1328 seem a simple task, like observe and make notations, or directly measure the

1329 function. However, in most cases there is limited knowledge of species’ natural

1330 history of species and/or research infrastructure (budget, laboratories, human

1331 resource). A common way to solve this problem and address functionality is to

1332 use characteristics of the species that may reflect directly or indirectly what they

1333 do or how do they respond: i.e. the use of functional traits (Calow, 1987). For a

1334 while, trait was used as a synonym of a lot of different things. The definition was

1335 structured by Violle et al. (Violle et al., 2007) and now a functional trait is defined

1336 as any measurable characteristic (morphological, biochemical, phenological,

1337 physiological, and behavioral) that is measured at the individual level that affects

1338 its fitness. Traits can also be decomposed in effect and response traits (Violle et

1339 al., 2007; Díaz et al., 2013). These traits are used assuming that such

1340 characteristics of species are adapted to their function and interactions (Madewell

1341 & Moczek, 2006). The studies of traits emerged primarily with plants,

1342 emphasizing traits with more direct relations with processes, like photosynthesis,

1343 and the large number of experiments did permit to evaluate other processes like

1344 decomposition (Moretti et al., 2017). The use of traits in functional ecology for

1345 animals is less disseminated although is growing, and emphasizes mostly the

1346 study of assembly processes. But is important to highlight the low rate of

1347 experiments in traits in animal functional ecology (Moretti et al., 2017; Noriega et

1348 al., 2018). Dung beetles, as an exception, is one of the groups where experiments

1349 had been carried out (i.e. sexual selection and with functional groups; (Emlen et

1350 al., 2005; Slade et al., 2016).

1351 This approach presented a revolution in the field, since it allowed working

1352 with multiple species, and to study species with little or unknown natural history

1353 information. In the early days of trait-based functional ecology, studies were

1354 asking how did the environment affect the traits or how do they affect the

1355 environment. But with the increase of human impact on natural areas and

1356 extinctions, it became more popular the studies using metrics to understand trait

1357 variation in communities and how do they affect the environment (McGill et al.,

1358 2006). Trait range and variation were used to measure the functional part of

1359 biodiversity, the functional diversity.

1360 Understanding the evolutionary history of traits permitted to analyze the

1361 assembly of communities and the patterns that promote coexistence. By this

1362 approach, the relationships between phylogeny and traits can explain the

1363 patterns of divergence or convergence of traits. Convergence of traits is mostly

1364 influenced by environmental filters, plus some biotic filters (e.g. predation,

1365 facilitation); divergence is influenced mostly by biotic filters (competition, sexual

1366 selection, character displacement) and environmental heterogeneity. Although in

1367 some cases phylogenetic diversity was treated as a proxy for trait diversity since

1368 it was assumed that all traits have some phylogenetic structure, and phylogenies

1369 provided more information. Pavoine & Bonsall (2011) showed that if we use the

1370 complementarity of both aspects of diversity, we could have greater advance in

1371 the understanding of community assembly. The variation in traits can be

1372 decomposed in four components, a) species environment, b) phylogeny, c) a

1373 shared component of environment and phylogeny, and d) an unexplained

1374 component (Diniz‐Filho & Bini, 2008). Each component reflects different

1375 processes of evolution, a higher degree of a represent a strong environmental

1376 filtering, a higher b represents strong niche conservatism, and a higher c reveals

1377 a long-time evolutionary constraint and stabilizing selection (Pavoine & Bonsall,

1378 2011).

1379 After all this discussion and analyzing every aspect of function is safe to

1380 define function as an effect of an action performed by a biological item or a

1381 response to other biotic and abiotic conditions in a given system. In a broad way,

1382 we can assume function as every interaction an individual does during his life,

1383 with the environment, and with other individuals. This may seem a concept so

1384 broad that it encloses everything but is very important to highlight that ecological

1385 function is very context dependent. So, there is a need to explicitly express the

1386 context of the ecological function you are measuring in your study.

1387

1388 Dung Beetle Natural History

1389 Dung Beetles are popular beetles, well known by their alimentary preferences for

1390 mammal excrements, and their peculiar behavior of making and rolling balls of

1391 dung. This name is referred to beetles of the subfamilies Scarabaeinae (Latreille,

1392 1802) and Aphodiinae (Leach, 1815) and the family Geotrupidae (Latreille, 1802),

1393 that shows coprophagous habits. The principal focus of this work will be in the

1394 Scarabaeinae because they are the most representative family in the

1395 assemblages of dung beetles in the Neotropics. The Aphodiinae are present in

1396 low abundance and Geotrupidae is restricted to high altitudes, like in the Andes.

1397 The subfamily Scarabaeinae includes about 6500 species present in

1398 almost all biomes on Earth (Schoolmeesters, 2019). It is important to understand

1399 the origin and evolutionary patterns of the family. The oldest record of the

1400 Scarabeoidea appears in the middle Jurassic, Alloioscarabeus cheni (Bai et al.,

1401 2012). Using molecular data, we can trace the origin of Scarabaeinae to

1402 approximately 118.8-131.6 MA (Gunter et al., 2016). The basal clades in the

1403 subfamily were originally saprophagous or mycetophagous, and dung-feeding

1404 behavior arose around mid-cretaceous with the exploration of dinosaurs’ dung

1405 and had a great diversification prior to mammalian diversity (Gunter et al., 2016).

1406 The extinction of dinosaurs affected dung beetles since it is unlikely that species

1407 that used the dung of the large dinosaurs could simply change its feeding habit

1408 to using dung from the small insectivore mammals that coexisted at the same

1409 time. So, the group survived most likely thanks to small generalist species that

1410 exploited the dung of small dinosaurs and small mammals (Gunter et al., 2016).

1411 The origin of the group was Gondwanian, approximately in what is today

1412 South Africa (Scholtz et al., 2009). The division of the continents promoted the

1413 first radiation of the dung beetles, as noted for the basal tribes (Dichotomini and

1414 Cantonini). Although both these tribes are of Afrotropical origin, currently their

1415 representatives can be found in almost all biogeographical regions (Monaghan et

1416 al., 2007). The early isolation promoted a high diversification in the group, as

1417 noted in the monophyletic origin of exclusive tribes and clades in South America

1418 (Eurysternini, Eucranini, Phaneini, Dichotomius, Canthon and Canthidium)

1419 (Monaghan et al., 2007; Gunter et al., 2016), Australásia (Monaghan et al., 2007),

1420 and Madagascar (as the most recent) (Gunter et al., 2016).

1421 Surviving by eating dung demanded some crucial adaptations that explain

1422 the success of these beetles (Box 1). One necessary adaptation is the highly

1423 specialized mouthparts. Different to most beetles that have biting mouthparts,

1424 dung beetles show a mandible adapted to soft eating, with bristles that act as

1425 filters and microstructures in the molar lobe that act as a grinder (Hanski &

1426 Cambefort, 1991). A feature that also shows adaptation is the female

1427 reproductive system, which presents only one ovariole or an atrophied ovariole

1428 and a big ovariole. This outcome in a small number of highly invested eggs to

1429 deal with the scarce and heterogeneous resource they use (Gonzalo Halffter et

1430 al., 2013).

1431 Another reproductive feature that helps to understand their success is

1432 parental care and, in some cases, the sub-social behavior. Most parental care in

1433 dung beetles is pre-oviposition, with the construction not only the nest but in some

1434 cases elaborated brood balls that protect the larvae (Gonzalo Halffter &

1435 Matthews, 1966). The sub-social behavior appears when the parental care goes

1436 beyond the nesting, when the parents remain in the nest, protecting and cleaning

1437 the brood containing the larvae (Leigh W. Simmons & Ridsdill-Smith, 2011).

1438 The eye of dung beetles also presents some modifications in comparison

1439 to other beetles: a prolongation of the gena (or cheek) that intrudes the eyes, and

1440 most of the time divides them. This prolongation is called canthus and its function

1441 is mainly to protect the eyes of the abrasion of digging, and when divide the eye

1442 is responsible to create different organs, one ventral for normal vision and one

1443 dorsal for navigation (Scholtz et al., 2009), extremely important in nocturnal dung

1444 beetles, as they use the milky way for localization at night (Dacke et al., 2013).

1445 If we study the origin of the superfamily Scarabaeoidea, the theory is that

1446 the family started as a group of saprophagous beetles, with further evolution in

1447 coprophagy in the subfamilies above cited (Gunter et al., 2016). The whole

1448 superfamily is identified by a presence of lamellate antennae (Gunter et al.,

1449 2016). This type of antennae can be another adaptation for an ephemeral

1450 resource, since the function of lamellate antennae is to increase the superficial

1451 area of the antenullae segments, increasing the olfactory reception, which can be

1452 crucial to find the resource as fast as possible. Even though it is a likely

1453 hypothesis, it has never been tested.

1454

Box1. Dung Beetle Morphology, adapted from Ratcliffe, 1991

Clypeus – Dorsal view: part of the head that are in front of frons; Ventral view: part of the head is most anterior; Cephalic Horn – Horn that occurs in the head; Gena – Lateral region of the head analogous to cheek; Teeth – Anterior legs tibial teeth; Frons – Upper portion of the head, behinf the clypeus in front of vertex; Pronotum – Dorsal surface of protórax; Pronotal Tubercles – protuberances that occur in the pronotum, may be exaggerated horns; Elytra – the anterior chitinous wings of beetles that protects the posterior wings; Coxa – the first segment of the leg; Trochanter – the second segment of the leg; Femur – the third segment of the leg; Tibia – the fourth segment of the leg; Tarsus – the foot; Tarsal claw – claw present in the tarsus; Spur – A spine-like appendage on the cuticle;

Mentum – the distal sclerite of the insect labium bearing the movable parts; Gula – sclerotized segment that link the mentum to the labium; Labial Palp – Smaller mandible appendage; Maxillary Palp – Bigger mandible appendage; Antennal Club – last antennae segments that forms the lamellas; Antennal Shaft – first antennae segment that links the antennae to the head; Prosternum – first toraxic sternite segment; Mesosternum – second toraxic sternite segment; Metasternum – third toraxic esternite segment; Abdominal sternites – ventral segments of the abdomen; Pygidium – the last abdominal segment;

1455 A very conspicuous characteristic in dung beetles is the presence and

1456 great variety of horns. Even Darwin (1871) was mesmerized by this trait and

1457 explained the evolution of them throughout the sexual selection theory. This

1458 feature is prominent and more developed in males, but in some species, the

1459 females show smaller horns. The principal use of the horn is to fight or defend

1460 something (the nest, the brood, the female), which may explain the conspicuity in

1461 males, however other functions are unknown until now. The size or the presence

1462 of the horn, in most cases represents a high rate of successful mating (Kotiaho,

1463 2002), but it does not translate directly in the quality of the male, since the size

1464 and production have high environmental causes (Hunt & Simmons, 1997), just

1465 as the production of the horn is the result of the number of available resources

1466 as larvae (Moczek, 1998). The female chooses a male with horns, because it

1467 represents a more successful development of the larvae by increasing the size

1468 of the brood mass, and defense of the nest (Hunt & Simmons, 1997), but

1469 represent some cost for the female since she creates a smaller number of brood

1470 masses For males having large horns also involved costs involved considering

1471 that the developing time of the larvae increases (Moczek & Emlen, 2000), the

1472 maneuverability in the tunnels reduces (Madewell & Moczek, 2006) and the

1473 position of the horn may decrease the resource invested for others parts of the

1474 beetle (Pizzo et al., 2012). Beetle’s horns appear mostly in four positions, on the

1475 base, center or front of the head and in several places in the thorax (Emlen et al.,

1476 2005). Depending on the region were the horn is developed there is a

1477 differentiated resource allocation which decreases the investment in other

1478 structures such as eyes (when it surfaces in the vertex, in the basal area of the

1479 head), antennae (when it surfaces in the front or the clypaeus, in the center and

1480 front of the head respectively), wings (when it surfaces in the middle or by the

1481 sides of the thorax) (Emlen et al., 2005), and in some species also the testes

1482 (Simmons & Emlen, 2006). But there are some special conditions when the

1483 benefits surpass the cost for horns. For example, horns on the vertex will have

1484 an advantage for diurnal beetles, as having smaller eyes does not represent a

1485 disadvantage contrary to nocturnal beetles (Emlen et al., 2005). In the forest

1486 where the high humidity maintains the chemicals in the air for a longer period, a

1487 reduction in the size of the antennae is not a disadvantage as it is in open areas

1488 (Emlen et al., 2005). The cost of thoracic horns is related to dispersal events,

1489 since they fly to dung pats one feature that may decrease the disadvantages of

1490 reducing maneuverability is the density of the population, since in a high-density

1491 population the probability of encounters is higher and with the increased sexual

1492 competition the investment in horns provide a higher success ratio (Emlen et al.,

1493 2005; Buzatto, Tomkins, & Simmons, 2012). The relation with horn and testes is

1494 not present in all species. This appears in species with dimorphic males, where

1495 there is a wide range from individuals without horns to individuals with fully-grown

1496 horns (Moczek, 1998). In this case, the investment in horns decreases the testes

1497 but it ensures the reproductive advantage by isolating and protecting the female.

1498 The hornless male assures his advantage by acting as a satellite male in two

1499 ways: a) He makes a secondary tunnel to find the female, copulates with her and

1500 then flee the nest, b) it simply passes by the bigger male pretending to be another

1501 female, copulates and then flee (Moczek & Emlen, 2000). Horns are also

1502 associated with the tunneling behavior, they are weapons appropriated to close

1503 spaces, and even the beetles that inhabit open spaces and present horns are

1504 derived from species with tunneling behavior (Emlen & Philips, 2006).

1505 A conspicuous characteristic of dung beetles is the rolling behavior. This

1506 behavior evolved at least five times independently in the group (Gunter et al.,

1507 2016). This explains the variety of rolling ways (alone, female on top male behind,

1508 male on top female behind and others, (Gonzalo Halffter & Matthews, 1966)).

1509 With the rolling habit came some adaptations especially in legs. The front legs

1510 present the typical burrowing beetle leg, with the presence of dentate tibiae

1511 (Crowson, 1981), and the middle legs present adaptations to the habit of rolling

1512 (Hanski & Cambefort, 1991). Beetles with tunneling behavior tend to have smaller

1513 and broader middle tibiae whereas beetles with rolling behavior tend to have

1514 larger and thinner tibiae (Vaz-de-Mello, pers. comm.). The extension of the

1515 middle legs arose for ball manipulation mostly for directing and controlling the

1516 rolling action (Gonzalo Halffter & Matthews, 1966). The habit of rolling presents

1517 another trade-off for dung beetle trait: the rolling and burrowing of the ball. Hanski

1518 & Cambefort (1991) hypothesized that the adaptation for a faster rolling comes if

1519 there is a decrease in the burrowing capacity, as seen in Neosisyphus spinipes

1520 (Thunberg, 1818), an African dung beetle which has lost completely the ability to

1521 burrow and only hide the brood ball with leaves and other content in the litter

1522 (Hanski & Cambefort, 1991).

1523 There is more than meets the eyes when we think about dung beetle

1524 functions. The first thing that comes to mind is the obvious dung-feeding habit

1525 that affects directly the nutrient cycling (Gonzalo Halffter & Matthews, 1966). By

1526 feeding on dung, they do not only diminish the amount of dung available for flies

1527 but also control parasites, since adults feed on eggs of nematode and cestode

1528 parasites as well as on bacteria (Gonzalo Halffter & Matthews, 1966; Hanski &

1529 Cambefort, 1991; Nichols et al., 2008). Similarly, by revolving and diminishing the

1530 dung in contact with the air and solar radiation, dung beetles contribute to

1531 diminishing the emission of CO2 in pastures (Slade et al., 2016). Since most of

1532 the species make a nest by excavating the soil, another function is the

1533 bioturbation of the soil, and by burrowing the feces they enhance the

1534 incorporation of NO3 in the soil (Bertone, 2004). By rolling the dung away and

1535 burrowing it, they act as secondary dispersers of seeds and enhance plant growth

1536 (Macqueen & Beirne, 1975; Miranda, Santos, & Bianchin, 2000; Andresen, 2002).

1537 Some beetles that present others feeding habits provide other functions such as

1538 pollination, observed in a small number of plants in families Aracea and Lowiace

1539 (Sakai & Inoue, 1999) that emulates the smell of dung to attract these beetles.

1540 More unusual than pollination is the example by Canthon virens (Mannerheim,

1541 1829), that predates on Atta spp. queens regulating the population dynamics of

1542 these species (Vasconcelos et al., 2006), and Deltochilum valgun (Burmeister,

1543 1873) that predates on diplopods (Larsen et al., 2009).

1544 Also, important, mostly in the Neotropics, is the necrophagy habit (Halffter

1545 & Matthews, 1966), highly present in the Phanaeini tribe (Edmonds & Zídek,

1546 2010). This habit is well documented in some species, although a little neglected

1547 in matters of function. Just as coprophagous beetles, they contribute with nutrient

1548 cycling and control of parasites and flies, additionally one of the most iconic

1549 functions from an anthropological point of view is the use of some species as

1550 forensic evidence (Pessôa & Lane, 1941; Almeida et al., 2015). Other species

1551 that presents saprophagous and fungivorous habits may participate mostly in the

1552 nutrient cycling.

1553 By analyzing the differences of behavior in the use of resources some

1554 authors had grouped these beetles in guilds. The first classification was proposed

1555 in an evolutive way by Halffter & Matthews (1966) and Halffter & Edmonds (1982).

1556 They studied the nesting behavior of dung beetles and identified four groups (Fig.

1557 1.1). Although well detailed and explained this classification was not used much.

1558

1559 Figure 2.1. Dung beetle nesting patterns and their evolutive trends as shown in Halffter & 1560 Edmonds, 1982

1561 The subsequent classifications used the feeding behavior to address the

1562 groups, first proposed by Bornemizsa (1969), he identified three principal groups,

1563 Telecoprids, also called rollers, the beetles who make balls and roll them away

1564 to someplace distant from the food source, Paracoprids, also called tunnellers,

1565 the ones that burrow the food directly below or near from the food source and

1566 Endocoprids, also called dwellers, the ones that feed directly on the source. This

1567 classification was then expanded by Doube (1990) in 7 groups, using not only the

1568 feeding behavior but also the size of the beetles and the speed of burrowing. The

1569 Telecoprids were divided into 2 groups using size to group them in Big and Small

1570 Telecoprids. The Paracoprids were divided into 3 groups using size and speed to

1571 classify them into Large Fast Paracoprids, Large Slow Paracoprid and Small

1572 Paracoprid. The Endocoprids remained intact, and he identified a new group

1573 called Cleptocoprids, beetles that explore nest or balls of other species. Although

1574 largely used and cited (128 times – Google Scholar) this classification was not

1575 always fully used, mostly because the characterization for fast and slow is not

1576 defined in the paper, and sometimes probably for a faster classification without

1577 the need to measure the beetles.

1578 The disadvantage of the classification is that in most cases we only know

1579 the identity of the species. With little biological information come generalizations,

1580 which can be dangerous and exclude information about the functionality of the

1581 species. The use of traits can resolve this problem. By using a group of characters

1582 that can be measured directly in the individual, we diminish the problem of

1583 species with unknown biology and can have a better understanding of their

1584 functionality.

1585 Dung Beetle Functional Traits 1586 So, we describe the characteristics of dung beetle related with their functionality;

1587 their traits. For a better understanding, I organized this section by the type of trait

1588 (morphological, behavioral, phenological and physiological) and focused mostly

1589 on adults because works with larvae are rare. Morphological traits are assumed

1590 here as any trait involving measures directly obtained in the individual body or

1591 structures. Behavioral is any trait which involves some kind of behavior, or

1592 strategy adopted by the individual. Phenological are any time involved trait.

1593 Lastly, physiological traits are traits involved with metabolic and physiologic rates.

1594 Morphological: 1595 Size (Figure 1.2.A): This trait is commonly used in ecological studies, because of

1596 easy access and because of the amount of information given by. Is common

1597 to use exclusion experiments to understand the effect and response of the

1598 trait (g.e. Slade et al., 2016). This trait may vary in function of the type of

1599 soil (argillous soils exclude small species), disturbances (larger species are

1600 more sensible for habitat disturbance), and rainfall (larger species appears

1601 when the rainy season started).

1602 Measurement: Measured by the direct weighing of the beetle (mass); the

1603 product of length, height and width of the body (volume); or by the linear

1604 measurement of the total length of the beetle (length). Length can also be

1605 measured by the sum of pronotum length and elytra length because is

1606 common to have variations of the head position when the individual is

1607 fixated.

1608 Effect: it may reflect the cost of development, the amount of resource

1609 utilized, competing advantages, mating success, amount of seed secondary

1610 dispersal, size of maximum seed dispersal, the capacity of revolving

1611 compacted soil, proxy to strength. I assume body mass, volume and length

1612 provide the same biological information because of the high correlation

1613 between them (Radtke & Williamson, 2005).

1614 Protibia area/size (Figure 1.2.B): Analogous to the area of a shovel. The front leg

1615 is used mostly for burrowing not only the soil but also the feces.

1616 Measurement: Length or total area of the structure.

1617 Effect: It may reflect indirectly in the speed of excavation. This trait has not

1618 been widely used therefore the relation is theoretical.

1619 Prothorax height (Figure 1.2.C): This is another trait involved with burrowing. The

1620 muscles of the front leg are inserted in this region, and the height of the

1621 prothorax reflects directly in the size of the muscle fiber used to move the

1622 leg.

1623 Measurement: This trait is measured from the base of the leg insertion (the

1624 coxa) to the highest part of the pronotum, excluding horns.

1625 Effect: It provides an indirect measure of the strength of the leg and may

1626 reflect the capacity of excavation in gradients of soil hardness.

1627 Mesotibia ratio (Figure 1.2.D): This ratio reflects a morphological pattern for the

1628 identification of rollers and tunnellers. Rollers have thin and elongated

1629 middle legs and tunnellers have short and broad legs (Vaz-de-Mello pers.

1630 com.). This relation was tested by Vaz-de-Mello in a work during his thesis

1631 but was never published.

1632 Measurement: apical width of mesotibia divided by the total length of

1633 mesotibia.

1634 Effect: It may reflect the competitive advantage to isolate the resource for

1635 feeding or mating.

1636 Metatibia length (Figure 1.2.E): The last leg of dung beetle is related to the rolling

1637 behavior. It serves as a control for direction and is used for giving speed on

1638 the ball (Gonzalo Halffter & Matthews, 1966; Hanski & Cambefort, 1991).

1639 Aside from observational descriptions, there are no experimental data.

1640 Measurement: Total length of the metatíbia.

1641 Effect: This trait is related to the speed of rolling.

1642 Wing load (Figure 1.2.F): Is assumed that with a higher wing load the individual

1643 will have a better sustentation when flying increasing the dispersal rate.

1644 Measurement: the ratio between the wing area and size or body mass.

1645 There is little experimentation on this trait (g.e. Larsen, Lopera, & Forsyth,

1646 2008)

1647 Effect: it provides an indirect measure of the dispersion capacity of the

1648 beetle.

1649 Number of sensillas per µm2 (Figure 1.2.G): dung beetle uses smell to find dung

1650 pats and mating partners. So, the antenna is a precious structure and

1651 provides a trait which corresponds to the olfactory capacity of the individual.

1652 Measure directly the antenna may not infer a sense of olfactory capacity

1653 because the principal structure in the antenna, the sensilla, may vary in

1654 density in each species, even in species with large antenna segments

1655 (Inouchi et al., 1987; Kim & Leal, 2000). So, the best way to assure the

1656 measure of olfactory capacity is to measure the number of sensillas per µm2

1657 or the sensilla density.

1658 Measurement: by electronic microscopy, selecting a small area and

1659 counting the number of sensillas per area.

1660 Effect: it reflects a measure of olfactory acuity, in a broad sense it measures

1661 the capacity of localizing the resource they utilize and this information is

1662 used to navigate ‘til getting in the resource.

1663 Eye area (Figure 1.2.H): Although the principal sense to find mates and resource

1664 is the olfact, the eyes are very important to dung beetle to navigation,

1665 obstacle avoidance, and orientation when flying and walking (Byrne &

1666 Dacke, 2011). There is some isolated experimentation with this trait (g. e.

1667 Dacke et al., 2013).

1668 Measurement: This trait can be measured by pictures of the head taking the

1669 area of the ventral and dorsal eye, or with the complete dissection of the

1670 eye for a measure of the total area.

1671 Effect: By assessing the eye area we can measure indirectly the navigation

1672 and orientation capability.

1673 Clypeus area/width (Figure 1.2.i): Since dung beetles use the head to aid the

1674 excavation process the clypeus is a good structure to infer a burrowing

1675 capability. It’s mostly used as a lever to detach some particles of soil and

1676 the ball of the feces. In the special case o Canthon virens, it is also used as

1677 a lever to decapitate the Atta queen. This trait presents observational

1678 studies only.

1679 Measurement: It is measured by the total area, or by the maximum width of

1680 the structure.

1681 Effect: This trait presents more information on the burrowing ability.

1682 Horns (Figure 1.2.J): As explained before horns are used mostly for a contest of

1683 males, or in rare cases of females contest for foods. In functional works, this

1684 trait has never been used.

1685 Measurement: For a community level I advise to use this trait as presence

1686 or absence since not all species present horns. If we use the trade-offs

1687 relations shown above, we can use also the location of the horn as a trait.

1688 Effect: His presence indicates contest for mating and contest for resources.

1689 It may reflect not only for a strong sexual selection by his presence, but de

1690 variation of his size can be used as an indirect indicator of mating success

1691 (intro population only).

1692 Response: The localization of the horn reveals some constraints and some

1693 ecological aspects where the horn is more advantageous as explicated

1694 above.

1695 Mouth (Figure 1.2.K): dung beetle feeds of small particles of dung, containing

1696 bacteria, epithelial cells of the gut of mammalians, parasites eggs, and

1697 excludes larges particles by filtering structures of the mandible.

1698 Measurement: We can measure this in two aspects, the area of the hard

1699 parts of the mandibula used for filtering, or measuring with experiments

1700 using particles with known size (g.e. Holter et al., 2002).

1701 Effect: This trait indicates the size of the particle ingested by a dung beetle,

1702 and correlating with the possible sizes of parasites eggs possible to be

1703 destroyed.

1704 Color (Figure 1.2.L): The coloration in dung beetle is not involved with toxicity,

1705 mostly with thermoregulation and cryptism with soil. The range of color

1706 varies between black to very iridescent colorful individuals. And some

1707 relations of this difference give support to use this trait as an indirect

1708 measure of daily activity. Hernández (2002) has shown is her study that

1709 there is a high correlation between daily activity, showing that diurnal

1710 species are more likely to be colorful and nocturnal species more likely to

1711 be black.

1712 Measurement: This trait is normally used in a binomial way as colorful or

1713 not. There are some studies by this matter (g.e. Vulinec, 1997; Hernández,

1714 2002).

1715 Response: The coloration is a response to predation in open areas, and to

1716 temperature variations, since in species that use open and forest habitats,

1717 the individuals present in open areas present colors more iridescent

1718 (Hernández pers. com.).

1719

1720 Figure 2.2: Morphological traits of dung beetles. A) Size, B) Protibia area, C) Prosternum Height, 1721 D) Mesotibia ratio, E) Metatibia length, F) Wing area, if you divide this by the size you have the 1722 wing load, G) antennal sensillas, H) Eye area - adapted from Pizzo et al., 2012, i) clypeaus width, 1723 J) Horn, K) mandibulae of a dung beetle, L) color variation of two species of dung beetles 1724 Coprophanaeus dardanus e Canthon podagricus.

1725 Behavioral: 1726 Reallocation behavior (Figure 1.3.A): This is the trait mostly used in dung beetle

1727 research. It comprehends the three principal guilds rollers, tunnellers and

1728 dwellers (Doube, 1990), given a rapid functional aspect of communities. In

1729 some cases, this trait is described as a nesting type, but I assume nesting

1730 as a behavior with a pure mating purpose, and this is also used for feeding.

1731 This trait is very used and has been experimented with exclusion bio

1732 essays, using some kind of wall to exclude rollers, a plastic square

1733 underneath the dung pat to exclude tunnelers (g.e. Slade et al., 2007; Braga

1734 et al., 2013).

1735 Measurement: this trait is categorically inferred by observational studies or

1736 by generalizations.

1737 Effect: It may reflect the competitive advantage to isolate the resource for

1738 feeding or mating.

1739 Nesting (Figure 1.3.B): This trait describes the nesting habits of dung beetles.

1740 Although it has some observational studies, it hasn’t been explored

1741 experimentally.

1742 Measurement: This trait can be described in two ways, in a binary way if do

1743 or do not nest, or in a more complex way describing some categories more

1744 derived of nesting, as some species that make a chamber of spherical or

1745 pear-shaped soil around the brood ball to protect the larvae ( Halffter &

1746 Matthews, 1966).

1747 Effect: This reflects the investment in reproduction or in larval care.

1748 Number of eggs (Figure 1.3.C): Most species put only one egg in a brood ball.

1749 But some species divide the brood ball in smaller amount with more eggs.

1750 This trait is not often used because of the necessity of reproduction of the

1751 beetles in laboratories.

1752 Measurement: This trait is commonly measured in the laboratory by creating

1753 the beetles and accessing the nest. For some species is possible to make

1754 literature reviews.

1755 Effect: An indirect measure of investment of larval development.

1756 Response: This trait may vary with the amount and quality of the resource.

1757 Burial depth (Figure 1.3.D): Very important for tunnellers since they compete

1758 directly for space underneath the dung pat.

1759 Measurement: It is measured by careful excavation of the tunnels or by

1760 studies in the laboratory.

1761 Effect: The burial depth may indicate the amount of bioturbation provided

1762 by the dung beetle.

1763 Response: It also indicates which species compete more directly and a

1764 response to soil hardness.

1765 Diet (Figure 1.3.E): This trait may reflect the preference in determinate feeding

1766 resource.

1767 Measurement: This trait is mostly used categorically, or by some

1768 experimentation in the field or by literature review. I advise using this trait in

1769 a continuous way because is easy to make experiments with different types

1770 of dungs and other resources. In this way, it can be measured by direct

1771 proportion or using indices of niche breadth like Levin’s index or IndVal.

1772 Effect: It reflects how much of the resource is utilized by the beetle on a

1773 broad scale and affects the nutrient cycling.

1774 Larval diet: In some cases, the adult feeds in one type of resource and provides

1775 another type for the larvae. This happens mostly in some necrophagous and

1776 some fungivorous beetles (Hanski & Cambefort, 1991). This trait is not

1777 much used because it involves the larvae.

1778 Measurement: It may be measured binary simply addressing the similarity

1779 between adult and larval diet, or categorically specifying the type of diet.

1780 Effect: Is significant to address this trait, since it amplifies the range of

1781 nutrients cycled by the individual/species.

1782 Habitat preference (Figure 1.3.F): There are a shift in community composition, for

1783 example, open and forested areas. Not only because of evolutionary history,

1784 but also as some ecological costs and trade-offs (Krell et al., 2003).

1785 Measurement: This trait is mostly used categorically, or by literature review.

1786 I advise using this trait in a continuous way because there is a low cost in

1787 the collecting of these beetles and they respond rapidly. In this way, it can

1788 be measured by direct proportion or using indices of niche breadth like

1789 Levin’s index or IndVal.

1790 Effect: This trait can be interpreted as how much the species contributes to

1791 the function of that habitat or the number of habitats that a species can

1792 occupy and perform.

1793 Thermoregulation (Figure 1.3.G): This trait involves some strategies the beetles

1794 utilizes to actively thermoregulate. The most common is the friction, or high

1795 activation of the wing without the opening of elytra, or during fly (J.R. Verdú,

1796 Arellano, & Numa, 2006), but there are others strategies like the use of

1797 different hours of day or by using the dung ball as a thermal refuge (Smolka

1798 et al., 2012). This trait is only described for some species.

1799 Measurement: This trait may be addressed categorically (by which type of

1800 strategy used) or can be measured with experiments and thermal cameras.

1801 Response: This reflects not only in flying capacity since it diminishes the

1802 effects of not propitious weather but also as a greater thermal range.

1803

1804 Figure 2.3: Behavioral traits of dung beetles. A) reallocation behavior, B) type of nest, C) number 1805 of eggs, D) burial depth, E) diet, F) habitat preference, G) thermoregulation. A, B, C and D 1806 modified from Doube, 1990. E and F from Hill, 1996. G modified from Verdú et al., 2012.

1807 Phenological: 1808 Lifespan (Figure 1.4.A): The time that an individual remains in the system may

1809 vary with a number of reasons; some species have only one reproductive

1810 event, other have more than one. This affects the time that the individual

1811 will be performing his functions. But in most species there isn’t enough

1812 information for this trait, making him not being used often.

1813 Measurement: It can be measured by creating them in the laboratory or with

1814 few species review literature.

1815 Effect: The complete lifespan of the individual reflects the amount of

1816 resource he will utilize and other functions he will execute trough out his life.

1817 Larval development (Figure 1.4.B): is normal in dung beetles to have a variation

1818 in time of larval development depending on the quantity of a resource

1819 (Tomkins et al., 2005). This is not a common trait.

1820 Measurement: This trait can be measured by creation in the laboratory or

1821 by assuming a linear relationship between time and body size of the adult

1822 in species previously studied.

1823 Effect: This reflects the amount of resource the larva utilizes to develop.

1824 Response: If the normal quantity of resource for the larvae is subdued is

1825 normal to have a shortened period of development, resulting in smaller

1826 individuals, or individuals with lesser health.

1827 Daily activity (Figure 1.4.C): The dung is a resource that doesn’t have a specific

1828 time to appear fresh, so although the time that the dung remains in direct

1829 air contact affect the colonization, this doesn’t affect the daily activity of the

1830 beetles. This trait is well utilized in studies because of the easy gathering of

1831 dung beetles. This trait is commonly used categorically.

1832 Measurement: By revisiting the traps in more hours it can be measured the

1833 time the species utilizes dung.

1834 Effect: This trait reflects the active time in performing the functions of the

1835 individual.

1836 Response: The cost required for the reallocation behavior and the high

1837 competition of the ephemeral resource are some factors that affect the

1838 temporal segregation of the dung beetle community (Krell et al., 2003).

1839 Seasonality (Figure 1.4.D): Dung beetles present distinct activities in throughout

1840 the year, mostly because of the temperature and humidity necessities of the

1841 species (for activation of the individual and for the utilization of the

1842 resource). The problem of this trait is the relative time dependence of the

1843 collects since it will need at least two or more years for a good description

1844 of the seasonality, the reason that is common to use this trait in ecological

1845 works that don’t address directly the functionality of dung beetles. This trait

1846 is mostly used in a categorical way.

1847 Measurement: the best way to measure this trait is bay collecting in the

1848 same place for at least two years to identify the seasonal patterns of the

1849 species.

1850 Response: this trait is a response to favorable ambient conditions for the

1851 adult and the larvae to survive.

1852

1853 Figure 2.4: Phenological Traits of Dung Beetles. A) Complete life cycle of dung beetles, B) Larval 1854 development time (the phases of 1 trought 4, until the emergence of the adult), C) Daily Activity, 1855 D) Seasonality. A and B from https://goo.gl/images/De50hb. C and D from Hernández, 2002.

1856

1857 Physiological: 1858 Activity temperature: This trait indicates the minimum energy for the activity of the

1859 beetle. It’s not often used.

1860 Measurement: It is measured in laboratories or by models made through

1861 field experiments.

1862 Response: this trait represents the minimum temperature for the individual

1863 start to perform his functions.

1864 Respiratory type: the type of respiration in insects may reflect the amount of time

1865 an individual can tolerate ambient with little oxygen and also reflect the

1866 water loss (Chown & Davis, 2003).

1867 Measurement: This trait is measured in the laboratory and not often used. It

1868 is addressed categorically with three types, continual, cyclic or

1869 discontinuous.

1870 Effect: It reflects the amount of time the beetle can remain buried in the dung

1871 and the rate of gas exchanges which can result in economy in water loss.

1872 Response: This trait is a response to high temperatures and to inundations.

1873 Thermal tolerance: The thermal tolerance indicates the maximum and minimum

1874 temperature that the beetle remains alive. This trait isn’t used regularly in

1875 dung beetle functional studies.

1876 Measurement: It is measured in experiments in the laboratory, by assessing

1877 the minimum and maximal temperature where half of the population dies.

1878 Effect: This trait reflects all the range of habitats where the beetle will be

1879 able to live (Verdú & Lobo, 2008).

1880 Response: This trait is a response to the thermal variance in habitats.

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1 CAPÍTULO 1 2

4 Capítulo 3 UNVEILING THE DRIVERS OF DUNG BEETLE LOCAL 5 SPECIES RICHNESS IN THE NEOTROPICS 6

7 Marcelo Bruno Pessôa¹,*, Fernanda Alves-Martins², Paulo De Marco

8 Junior¹ and Joaquín Hortal¹,²

9 1 Departamento de Ecologia, Instituto de Ciências Biológicas, Universidade 10 Federal de Goiás, Avenida Esperança s/n, Campus Samambaia, ICB 5, CEP 11 74690-900, Goiânia, Goiás, Brazil 12 2 Department of Biogeography and Global Change, Museo Nacional de 13 Ciencias Naturales (MNCN-CSIC), C/José Gutierrez Abascal 2, 28006 Madrid, 14 Spain 15 *Author for correspondence. E-mail: [email protected] 16 17 Strapline: Drivers of Neotropical dung beetle species richness 18

19 Abstract

20 Aim: To identify the main drivers of local dung beetle species richness in the

21 Neotropics

22 Location: Neotropics

23 Major Taxa Studied: Dung Beetles (Coleoptera: Scarabaeinae)

24 Methods: We used a multi-hypothesis approach to understand which set of

25 hypotheses better-explained dung beetle species richness at a local scale. For

26 this, we surveyed published literature on dung beetle communities to extract

27 information on species richness, abundance, type of bait, type of habitat and

28 sampling effort (as hours/pitfall). Sites with low sampling effort were discarded.

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29 We used environmental variables to account for six hypotheses: productivity,

30 water–energy, ambient energy, habitat heterogeneity, climatic heterogeneity, and

31 resource heterogeneity, plus a seventh neutral hypothesis described using only

32 spatial data. Then we used mixed models, with abundance, ecoregion and bait

33 type as random factors, to select the best model among the variables pertaining

34 to each one of these hypotheses. Finally, we used structural equation models to

35 identify which factors explain dung beetle diversity in the Neotropics.

36 Results: Habitat heterogeneity was the single hypothesis that explained better

37 local variations in dung beetle richness. However, using a multi-hypothesis

38 approach the best model comprises four different hypotheses: productivity, water

39 energy, habitat heterogeneity, and resource heterogeneity. Structural equation

40 models showed that abundance has the greater direct (positive) effect on dung

41 beetle richness, followed by primary productivity and mammal richness – a proxy

42 for resource heterogeneity, together with the direct and indirect effects of soil

43 variables.

44 Main Conclusions: Several hypotheses need to be considered to account for

45 Scarabaeinae local richness patterns. The diversity of dung beetle communities

46 is determined by the interaction of water–energy dynamics and heterogeneity in

47 both resources and habitats. However, while the effects of heterogeneity are

48 direct on richness, energy acts through abundance, and water through resource

49 diversity.

50 Keywords: dung beetles, Neotropics, species richness, water-energy dynamics,

51 habitat heterogeneity, resource availability.

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53 Introduction

54 Geographical gradients of species richness are, perhaps, the oldest pattern

55 investigated by ecology and biogeography (Hawkins, 2001). Why and wherefore

56 some places host more species than others has long been subject to debate.

57 Strikingly, since Alexander von Humboldt (1850) noticed that there are more

58 species in the tropics than in temperate regions and tried to explain it with

59 recourse to climate and species resistance to freezing, almost 40 hypotheses

60 have been put forward to explain the latitudinal diversity gradient – based on

61 climate, habitat, physiological responses, and many other factors (Pianka, 1966;

62 Hawkins et al., 2003; Hawkins, 2008). This indicates that geographical diversity

63 gradients are the result of a complex array of factors affecting organisms in

64 different ways. Thus, finding generality in ecology needs to embrace multiple

65 aspects and use different variables (Lawton, 1999). Problems arise when each

66 hypothesis is understood as the sole driver of the gradient, treating them as rivals

67 rather than complementary. Avoiding this requires adopting a multi-hypothesis

68 framework, that treats the different hypotheses explicitly as complementary rather

69 than as isolated answers. Such approach can provide general explanations for

70 the origin of biodiversity patterns.

71 Dung beetles (Coleoptera, Scarabaeinae) are known to be affected by

72 multiple factors across scales. They respond rapidly to environmental changes,

73 are easy to survey, and play important ecosystem functions (Gardner et al., 2008;

74 Nichols et al., 2008). Indeed, the drivers of dung beetle diversity gradients in

75 temperate regions are relatively well known (Hortal et al., 2011). However, in the

76 Neotropics knowledge on their diversity is limited to studies at the local scale, and

77 the determinants of geographical variations in their communities cannot be easily

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78 extrapolated from other regions. Neotropical dung beetles present certain

79 particularities (Scholtz et al., 2009) since their evolutionary radiation in this region

80 is linked to forest habitats, in contrast with the Afrotropical and Palearctic history

81 of grassland evolution (Monaghan et al., 2007; Gunter et al., 2016). That said,

82 several climatic and environmental variables are known to affect dung beetle

83 diversity, including temperature, habitat changes, mammal diversity or

84 seasonality. This latter factor has a strong influence on dung beetle richness

85 since in dry seasons dung beetles are less active and represent a sub-sample of

86 rainy season communities (Hanski & Cambefort, 1991). This knowledge enables

87 constructing hypotheses that include not only which variables may affect

88 richness, but also the nature and direction of their effects. This allows putting

89 together multiple hypotheses into a single evaluation framework.

90 Here we aim to identify the main drivers of local dung beetle species

91 richness in the Neotropics. To do this, we use a comprehensive compilation of

92 local studies. We first evaluate multiple hypotheses within a single analytical

93 framework, using model selection analyses. More precisely, we evaluated six

94 hypotheses about the origin of diversity gradients: Primary Productivity, Water–

95 Energy, Ambient Energy, Habitat Heterogeneity, Temporal Heterogeneity, and

96 Resource Heterogeneity (see Table 1 below). Then, we evaluate the

97 relationships between the most informative hypotheses and dung beetle

98 abundance and diversity altogether using structural equation models. This way

99 we assess not only which hypotheses are more explanatory, but also their

100 relationship with the more-individuals hypothesis (through abundance; see

101 Wright, 1983; Storch et al., 2018), whether they explain dung beetle local richness

107

102 better in isolation or jointly, identifying the relationships between the variables

103 that comprise them.

104 Methods

105 Data collection and filtering

106 Dung beetles have been surveyed in different places for a long time in the

107 Neotropics, making possible to compile a database that represents the variations

108 in local diversity throughout this region. To construct the database, we searched

109 for ecological works on dung beetles in Web of Science and Google Scholar with

110 the following keywords: “dung beetle*”, “Scarabaeinae”, and the names of all

111 the countries which comprise the neotropical region. We only selected works that

112 were based on field surveys with baited pitfall traps (a standard for dung beetle

113 collection), presented a list of all dung beetle species recorded, and for which

114 information on the location of the surveys could be georeferenced with a spatial

115 precision of, at least, 10 km. From each work we extracted information about the

116 number of species recorded, total abundance, type of habitat (classified in open

117 or closed), type of bait used (classified in omnivorous, herbivorous, rotten fruit

118 and rotten meat), year, trap hours (calculated by the number of pitfall traps*time

119 in the field*number of collects), and country.

120 We obtained a total of 266 works in the literature survey(Table S1-1). Brazil

121 and Mexico where the countries with more works (84 and 73 prior to filtering,

122 respectively), perhaps due to their long tradition of research on dung beetle

123 ecology. From this initial list of works, we filtered out all works that had less them

124 960 trap hours, surveys were conducted before 1999, and/or lacked any of the

125 required information. After this process, 167 works located throughout most of

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126 the Neotropics – except Austral South America – were selected for further

127 analyses (Fig. 2.1)(Table S1-2).

128

129 130 Figure 3.1: Location of all dung beetle studies in the Neotropics found through our literature 131 search. In red, studies not used in the analysis and in black, studies used in the analysis. 132

133 Predictor Variables and Hypotheses

134 We used well-established climatic and environmental databases to extract

135 information on a series of predictor variables using QGIS (QGIS Development

136 Team, 2019). We extracted climatic data from WorldClim 2.0 database (Fick &

137 Hijmans, 2017), and actual evapotranspiration (AET) from CGIAR (Trabuco &

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138 Zomer, 2010), both at a resolution of 30 arcseconds. For Normalized Difference

139 Vegetation Index (NDVI) we used the NASA-MODIS database (Didan, 2015) at

140 a resolution of 10 minutes, using the annual mean from the year of the survey for

141 each study; for works performed in more than one year, we estimated the average

142 NDVI for all years surveyed. To account for habitat diversity we calculated land

143 cover richness as the number of land cover categories in a buffer of a 10km radius

144 around the georeferenced point of each study, using the Global Land Cover by

145 National Mapping Organizations (GLCNMO Version 3; Tateishi et al., 2014) at a

146 resolution of 15 arcseconds. In addition, data on habitat type were collected in

147 each article and classified in open, closed or both when the study did not provide

148 a list with separate types of habitats. We extracted the maximum and minimum

149 altitude from FAO elevation database (Fischer et al., 2012), using the same buffer

150 as for land cover at a resolution of 30 arcseconds. For soil structure, we extracted

151 the proportion of sand, silt, clay, coarse fragments, and bulk density, at three

152 depths: 0.15m, 0.6m, and 2m, at a resolution of 250m from the World Soil

153 Information database (Hengl et al., 2017). Then we performed a PCA and used

154 the broken stick criterion to choose the most representative axis as a measure of

155 soil structure. To account for mammal species richness in each study we used

156 Biodiversitymapping data (Pimm et al., 2014) at a 10km resolution, excluding all

157 volant and marine mammals. Finally, since several ecological and historical

158 processes may shape the patterns analyzed beyond environmental variations

159 creating spatial structure in the data, we used the latitude and longitude of each

160 study to construct a Trend Surface Analysis (Legendre & Legendre, 1998; Hortal

161 et al., 2008) and spatial eigenvectors (Diniz-Filho & Bini, 2005).

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162 We evaluated the adequacy of six main hypotheses to explain local dung

163 beetle species richness in the Neotropics, namely: Primary Productivity, Water

164 Energy, Ambient Energy, Habitat Heterogeneity, Temporal Heterogeneity,

165 Resource Heterogeneity (see Table 1 for details). To account for these

166 hypotheses, we constructed models based on the environmental predictors

167 described above, as follows. For primary productivity, we selected AET plus

168 NDVI. For water–energy we used AET plus annual precipitation, precipitation of

169 wettest quarter and precipitation of the driest quarter. For ambient energy, we

170 selected annual mean temperature plus mean temperature of the warmest

171 quarter and mean temperature of the coldest month. For habitat heterogeneity,

172 we used land cover richness plus altitudinal range, PCA axis of soil structure and

173 type of habitat. For temporal heterogeneity, we used Isothermality plus

174 Temperature Seasonality and Precipitation Seasonality. For resource

175 heterogeneity, we used Mammal richness. And to account for other spatially-

176 structured factors we used Trend Surface Analysis and eigenvector-based spatial

177 filtering to include the spatial data in the models (Diniz-Filho & Bini, 2005). See

178 Table 1 for further details. We conducted all analyses in R environment, using the

179 packages vegan, lme4, and NLME.

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180 proposed, originally were they where reference the including work, this in evaluated gradients diversity geographic of origin the on Hypotheses 3.1. Table Evapotranspiration, Actual for stands AET ofthem. one for each toaccount work inthis used variables the and studied, or proposed initially the variables Evapotranspiration. Potential PET for and Production, Primary Net Total for TNPP 112

181 Statistical analyses

182 We used generalized linear mixed models (GLMM) to assess the explanatory

183 ability of all hypotheses in a multimodel approach (Harrison et al., 2018). Since

184 abundance, type of bait and ecoregion can strongly influence the observed

185 richness of the studied local communities, we included the first variable as a

186 cofactor and the latter two as random factors in the GLMM to control of their

187 effects on the multimodel selection. We did not use sampling effort as a cofactor

188 because, in a preliminary analysis, traps/hour had a low effect on richness and

189 abundance (r2 =0,06 for richness, r2 =0,05 for abundance, Supplementary Figure

190 S1.1) once the localities with low sampling effort were discarded (see above).

191 Models were constructed by subsequentially adding hypotheses. That is, we

192 evaluated each hypothesis individually, and then all subsequent combinations of

193 two, three, four, five, and six hypotheses. In each step, we analyzed the

194 difference between the models with an analysis of variance (ANOVA) and

195 selected the most informative model as the one with the lowest Akaike

196 Information Criterion (AIC; Burnham & Anderson, 2002). In the last step, we

197 compared the lowest AIC of each model in the previous step, with the model with

198 all the hypotheses.

199 We used structural equation models (SEM; Grace, 2006; Shipley, 2016) to

200 evaluate the relationships among the different concurrent hypotheses and dung

201 beetle diversity. We included all hypotheses in a conceptual model that accounts

202 for the relationships between dung beetle richness and abundance, and the

203 variables used to describe all six hypotheses (Fig. 2). Different from the

204 multimodel hypothesis, for SEM analyses we did not use the isolated climatic

205 variables. Instead, we used a Principal Components Analysis to summarize total

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206 climatic variation into a limited set of uncorrelated factors, using the broken stick

207 criteria to determine the number of axes retained. To construct the conceptual

208 model, we took into consideration the resolution of variables and the known

209 relationships between them (see Table 2-1 and Hawkins et al., 2003; Schemske

210 et al., 2009; Storch et al., 2018). Based on this, we assume that mammal

211 richness, the variable with the coarsest spatial resolution was only affected by

212 climate and by land cover richness (taking into account that this variable was

213 calculated with a buffer to decrease its resolution). Importantly, since our data on

214 resource availability did not have any measure of quantity, we did not assume a

215 relationship between mammal richness and dung beetle abundance. We

216 performed the SEM framework in a piecewise approach (Shipley, 2009;

217 Lefcheck, 2016). We applied a test of directional separation (d-separation) to

218 evaluate if the relationships hypothesized in this initial conceptual model (see Fig.

219 2.2) fits the observed data (Shipley, 2000, 2009). If any relationships were

220 missing from the model, then we added new paths (i.e. new relationships) until it

221 fitted the observed data (reached by a d-sep > 0.05, Shipley, 2009) and excluded

222 non-significant paths, until all remaining variables are informative (see Calatayud

223 et al., 2016 for a similar approach). Then we interpreted the final clean structural

224 model.

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225 226 Figure 3.2: Prior conceptual model of the relationships between dung beetle richness and 227 geographical gradients hypotheses variables. 228

229 Results

230 The 167 local communities used in the analyses show a clear pattern of higher

231 species richness in the tropical region, with a decrease towards Mesoamerica

232 and Subtropical South America (Fig. 2.3). Local richness varies from 1 to 105,

233 being highest at a single site in Ecuador, in the Eastern Cordillera Real Montane

234 Forest ecoregion. Seventy-eight percent of these studies presented data from

235 forest habitats (including works that surveyed both forest and pastures), whilst

236 Omnivorous was the most common type of bait (65%).

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237 238 Figure 3.3 Dung beetle local species richness in the Neotropical Region. Increasing species 239 richness is depicted in progressively larger circles and on a continuous scale from white (lowest 240 richness) to red (highest richness). Species richness values have been Log10 transformed. 241

242 Multi-hypothesis testing

243 After evaluating all hypotheses in isolation, we found that ambient

244 heterogeneity presented the lowest AIC in comparison to the other hypotheses.

245 We then evaluated all their possible combinations, finding that the model joining

246 Productivity, Water–Energy, Ambient Heterogeneity and Resource

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247 Heterogeneity provides the largest decrease in AIC, and is, therefore, the most

248 informative (Table 2-2).

249 Table 3.2 Multi-models result in isolated hypotheses and the most explicative combination. PP – 250 Productivity hypothesis; WE – Water-Energy hypothesis; AH- Ambient Heterogeneity hypothesis; 251 RH – Resource Heterogeneity hypothesis.

252 253 Structural Equation Model

254 The overall SEM model based on the macroecological hypothesis

255 explained 85% of dung beetle richness on Neotropics. An explicit analysis of the

256 relationships between the variables pertaining to different hypotheses allowed to

257 identify the existence of a complex array of interactions, from which the strong

258 positive influence of abundance on dung beetle species richness outstands (Fig.

259 2.4). Besides that, major relationship, NDVI also showed a direct positive effect.

260 The influences of the type of habitat and Soil2 (a PCA axis related to Bulk density)

261 were indirect through their influence on abundance. And the same happens with

262 Climate2 (a PCA axis related to precipitation) and Land Cover richness, which

263 affect dung beetle richness indirectly through their effect on Mammal richness.

264 Climate1 (a PCA axis related to temperature) and Mammal richness presented

265 both direct and indirect effects through abundance. Whilst Soil3 (a PCA axis

266 related to CRFVOL – the volume of soil particles larger than sand) affected dung

267 beetle richness directly and through its effect on Mammal Richness (Fig. 2.4,

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268 Supplementary Figure S1.2; 1.3; 1.4). Since Abundance is affected by Mammal

269 richness, we can also interpret that Land Cover Richness, Climate2, and Soil3

270 also have indirect effects on dung beetle abundance.

271

272 273 Figure 3.4. Structural equation model for dung beetle richness on the Neotropics. The arrows 274 indicate the significant paths with their respective standardized coefficients (numbers) Black 275 arrows denote positive relationships and red arrows denote negative relationships.

276

277 Discussion

278 Our results evidence that the geographical variations in the species richness of

279 dung beetle communities in the Neotropics cannot be explained with recourse to

280 a single hypothesis. The explicit treatment of multiple hypotheses through multi-

281 model and structural equation modeling analytical frameworks allowed us to

282 pinpoint the complexity of interactions among climatic, soil and habitat variables

283 and mammal richness that influence local dung beetle diversity. Strikingly, the

284 strong direct effects of abundance and, to a less extent, productivity indicate that

285 local richness is primarily determined by the amount of energy and resources.

286 However, soil, habitat, and climate also present multiple direct and indirect effects

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287 on richness. In fact, the results of the multiple hypothesis evaluation identify

288 habitat heterogeneity as the most informative hypothesis, rather than one of the

289 species-energy hypotheses, as it would have been expected given the

290 importance of abundance (which is unavoidably determined by productivity;

291 Wright, 1983).

292 That habitat heterogeneity is the individual hypothesis with the strongest

293 influence on dung beetle local richness is not surprising. (Micro)climatic

294 conditions exert a strong influence on dung beetle activity, affecting their

295 metabolic rates and altering resource volatility and availability time (Davis et al.,

296 2013; Medina & Lopes, 2014). Further, most Neotropical Scarabeinae species

297 have evolved to exploit tropical and subtropical forest environments (Monaghan

298 et al., 2007; Scholtz et al., 2009; Gunter et al., 2016). Due to this, dung beetle

299 communities are strongly dependent on habitat structure (Hanski & Cambefort,

300 1991; Nichols et al., 2007; Gardner et al., 2008; Martello, Andriolli, de Souza,

301 Dodonov, & Ribeiro, 2016) and the microclimatic differences induced by the

302 variations in forest structure (da Silva & Hernández, 2016) and elevation (Nunes,

303 Braga, Figueira, Neves, & Fernandes, 2016). Further, dung beetles are also

304 affected by soil conditions, since their soil burial behavior (Halffter & Edmonds,

305 1982) imposes a selection pressure in relation to soil structure (i.e. the amount of

306 clay, sand and silt, and soil compaction; Davis, 1996).

307 The strong effects of habitat on local richness are modified by climatic and

308 productivity gradients. Besides habitat structure, the availability of resources,

309 energy, and water affects richness by regulating productivity, altering individual

310 metabolic rates, and increasing niche packing. The relative importance of these

311 factors varies geographically. In this sense, the importance of the water-energy

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312 hypothesis (i.e. that species richness is determined by a balance of water

313 availability and ambient energy) is also supported by our results. Following this

314 hypothesis, tropical areas are expected to be more affected by water availability

315 because population growth is typically not limited by temperature in these areas

316 (Hawkins et al., 2003; Tshikae, Davis, & Scholtz, 2013), while in temperate

317 regions temperature is the main constraint to dung beetle diversity (Lobo et al.,

318 2002; Hortal et al., 2011). Indeed, the effects of rain on the composition and

319 richness of Neotropical dung beetle communities are well known (Liberal et al.,

320 2011; Novais et al., 2016). However, in our SEM only temperature variables

321 (Climate PCA axis1) present a direct effect on richness, while the effects of water

322 variables (climate PCA axis 2), though stronger, were indirect through their

323 influence on mammal diversity. Therefore, although our results do not call for a

324 change in the hierarchy of the importance of the effects of water and energy

325 variables for this group, they do challenge the simple interpretation that limitations

326 to water metabolism of dung beetles impose a major constraint to their diversity.

327 Rather, such influence may be related to either the constraints imposed by water

328 metabolism to mammal diversity, the effects of water availability on the

329 attractiveness, texture and nutrient accessibility of mammal dung, or both.

330 Scarabaeinae communities present a strong relationship with mammal

331 communities because of their use of dung as their primary resource for feeding

332 and nesting (Halffter & Matthews, 1966). Although human feces are used by most

333 species in the Neotropics, all mammals have some dung beetle species

334 specialized in their excrements (Halffter & Matthews, 1966; Howden & Nealis,

335 1975), so neotropical dung beetles are determined by mammal diversity. Indeed,

336 dung beetle richness and abundance decline when mammal richness declines

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337 (Nichols et al., 2009) or when native mammal species are substituted by exotic

338 mammals (Filgueiras et al., 2009). This interdependence with mammals is

339 confirmed by the direct effects found in our structural equation models, and the

340 presence of the resource heterogeneity hypothesis in the joint multi-hypothesis

341 model.

342 Regardless of the importance of other factors, the strong effect of

343 abundance, which outstands in the results of our structural equation models, calls

344 for a major influence of productivity and resource availability on local

345 Scarabaeinae richness. Most of the influence of all the factors discussed above

346 is mediated by their effects on abundance. This indicates that the main

347 mechanism driving dung beetle local richness is the species-energy relationship,

348 as outlined by the more individuals’ hypothesis (Wright, 1983). According to this

349 hypothesis, high energy allows sustaining larger populations, diminishing

350 extinction rates, and increasing niche packing, permitting the coexistence of

351 larger numbers of species (Evans et al., 2005). Note here that this strong effect

352 of abundance was not explicitly evaluated in our multi-hypothesis evaluation

353 framework since abundance was included as a cofactor in all these models.

354 Given SEM results, we can assume that the general effect of abundance on dung

355 beetle diversity extends over all the hypotheses evaluated in this work, thus

356 providing support for its role as the main regulator of the effects of productivity on

357 species richness (Storch et al., 2018).

358 The community data compiled in our database comes from local dung

359 beetle inventories sampled using a large variability of survey methodologies and

360 sampling efforts. In many cases, surveys were poorly described, lacking a clear

361 description of the locality, habitat and/or method of capture. In some cases, such

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362 limitations prevented from distinguishing the abundances of each species in each

363 particular type of habitat, or the individuals captured with different kinds of bait.

364 This hampers a more in-depth assessment of several important factors that

365 operate at the local scale but does not affect our ability to assess variables

366 operating at larges scales. Indeed, the a priori selection of sites surveyed with a

367 sufficiently large sampling effort (in our case, more than 20 baited traps set up for

368 two days) ensures that all inventories considered for our analyses were

369 reasonably complete. This makes unlikely the existence of any consistent bias in

370 the data, beyond the obvious exclusion of Austral South America from the domain

371 of our analyses. Thus, although future works based on more detailed data on the

372 location and characteristics of the surveyed sites may enhance our knowledge

373 on local effects on dung beetle diversity, we would not expect significant changes

374 in the structure of relationships among large-scale factors and dung beetle local

375 diversity identified in this work.

376 Conclusions

377 Ecological communities are complex systems where multiple mechanisms

378 operate at the same time. Such complexity may create apparently incongruent

379 patterns in different localities and regions. Therefore, a joint analysis of the

380 different hypotheses raised to account for diversity gradients can provide a better

381 understanding of their origin. In this work, four out of the seven hypotheses

382 evaluated need to be considered to account for the local species richness

383 patterns of Neotropical Scarabaeinae. The diversity of dung beetle communities

384 in the region studied can be explained by a combination of the primary

385 productivity, water–energy balance, habitat heterogeneity, and resource

386 heterogeneity hypotheses. The influence of energy, water, soil, and trophic

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387 resources on occurs both directly and, especially, indirectly through the effects of

388 these factors on abundance and mammal diversity. In accordance with Storch et

389 al. (2018), energy mainly operates through abundance –arguably, the most

390 important direct determinant of richness in our analyses. Whereas the influence

391 of water on dung beetle diversity is mediated by its effect on the resources –in

392 this case, mammal diversity which most likely operates as a proxy of the diversity

393 of the feces and carrion from which Scarabaeinae feed upon. Taking this

394 comprehensive picture into consideration, in the tropical and subtropical areas of

395 Meso- and South America the species richness of dung beetle communities can

396 be primarily explained by the more-individuals hypothesis (Wright, 1983), through

397 both productivity (Storch et al., 2018) and niche packing (Hutchinson, 1959).

398

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1 Capítulo 4 Capítulo 2 2

4 ASSESSING THE GEOGRAPHICAL VARIATIONS IN THE

5 DETERMINANTS OF DUNG BEETLE LOCAL SPECIES

6 RICHNESS ACROSS THE NEOTROPICS

9 Marcelo Bruno Pessôa¹,*, Elisa Barreto¹, Viviana Alarcón, Tatiana

10 Souza do Amaral1, Lucrecia Arellano Gámez, Juliano André Bogoni,

11 Renata Calixto Campos, César M. A. Correa, Federico Escobar,

12 Veronica Espinoza, Malva Isabel Medina Hernandez, Elizabeth

13 Nichols, Jorge A. Noriega, José D. Pablo-Cea, Sebastián Villada-

14 Bedoya, Paulo De Marco Junior¹ and Joaquín Hortal¹,²

15 1 Departamento de Ecologia, Instituto de Ciências Biológicas, Universidade 16 Federal de Goiás, Avenida Esperança s/n, Campus Samambaia, ICB 5, CEP 17 74690-900, Goiânia, Goiás, Brazil 18 2 Department of Biogeography and Global Change, Museo Nacional de 19 Ciencias Naturales (MNCN-CSIC), Madrid, Spain 20 *Author for correspondence. E-mail: [email protected] 21

23 Abstract 24 Aim: To assess whether the relative importance of the drivers of local dung beetle 25 species richness varies geographically 26 Location: Neotropical Realm 27 Major Taxa Studied: Dung Beetles (Coleoptera: Scarabaeinae) 28 Methods: We compiled data from standardized surveys based on pitfall traps, 29 and estimated species richness at each locality using sample coverage

129

30 estimators. We related several predictors (including climate, habitat and mammal 31 diversity) with species richness and between them by means of structural 32 equation geographically weighted mixed models, with bait type as a random 33 factor. This approach allows analyzing the geographical variations in the factors 34 affecting dung beetle diversity, identifying the main driver of richness at each site, 35 and also assessing the eventual heterogeneity in its responses to these factors. 36 Results: Abundance was the main driver of dung beetle diversity throughout all 37 studied regions. Mammal diversity and Climate were consistently the main drivers 38 of dung beetle abundance. Richess responses to other variables were 39 heterogeneous, generating different structural path equations in three distinct 40 regions: Meso-America, Amazonia and Subtropical South America. Mammal 41 diversity contributed to dung beetle diversity and abundance differently, mainly 42 as a consequence of the conversion of forest to pastures. 43 Main Conclusions: Dung beetle diversity is consistently determined by the 44 mechanisms proposed in the More Individuals Hypothesis, where diversity is a 45 result of increased abundance through higher productivity. The heterogeneity in 46 the responses to other factors may be a result of different historical and 47 evolutionary processes that lead to the formation of the different regional 48 communities.

49 Keywords: dung beetles, Neotropics, species richness, more individuals 50 hypothesis, habitat heterogeneity, resource availability, Regional community 51 concept

130

53 Introduction 54 One of the main goals of science is to understand the variance of natural patterns.

55 Perhaps the first pattern formally described in ecology is the geographical

56 variation of species diversity (Hawkins, 2001). A number of hypotheses have

57 been raised to account for the origin of species richness gradients, focused on

58 climate, habitat, physiological responses, and many other factors (Pianka, 1966;

59 Hawkins, 2001; Hawkins et al., 2003; Hawkins, 2008). Such a large number of

60 hypotheses comes from the fact that the spatial variations of diversity are the

61 outcome of a complex system of factors with numerous processes occurring at

62 the same time. So, a framework considering the multitude of variables that could

63 be affecting diversity may be a good approach to understand the origin of the

64 geographical variations in species richness (Pontarp et al., 2018).

65 Beyond the existence of multiple drivers of diversity, another aspect should

66 be addressed to understand the complexity of the richness gradient. As observed

67 by Hawkins et al. (2003) in his reformulation of the water-energy hypothesis, the

68 importance of variables affecting biodiversity varies between different regions;

69 species richness is modulated by the availability of water in the tropics and by

70 energy availability in the temperate regions. This implies that the importance

71 and/or the effect of the variables may change in space (Cassemiro et al., 2007;

72 Gouveia et al., 2013), especially if we consider that each region or locality has a

73 different geological history and/or species pool evolution (Ricklefs, 1987;

74 Ricklefs, 2015).

75 Dung beetles (Coleoptera: Scarabaeinae) respond rapidly to

76 environmental changes, are easy to survey and play important functions in the

77 ecosystems (Gardner et al. 2008, Nichols et al. 2008). Several climatic and

78 environmental variables are known to affect dung beetle diversity, including

131

79 temperature, seasonality, mammal diversity and type of habitat (Krell et al., 2003;

80 Filgueiras et al., 2009; de Siqueira Neves et al., 2010; Giménez Gómez et al.,

81 2018; Chapter 1 of this thesis). This multifactor influence in dung beetles local

82 richness may be due not only to the complex functioning of their local

83 communities, but also the spatial variance in the importance and effects of

84 different factors. Indeed, the geographical variations in the drivers of dung beetle

85 diversity in temperate regions are relatively well known (Hortal et al., 2011). In

86 the Neotropics, however, very few studies on the determinants of the diversity of

87 this group go beyond the local scale (but see Chapter 1). Neotropical dung

88 beetles have certain particularities when compared with other regions. Their

89 evolution and radiation in Neotropics are linked to forest habitats, in contrast with

90 the Afrotropical and Palearctic history of grassland evolution (Monaghan et al.,

91 2007; Gunter et al., 2016).

92 Our aim in this paper is to assess whether the importance of the multiple

93 drivers that affect local dung beetle richness varies in space. To do this, we

94 combine high-quality local data from standardized surveys with geographically

95 weighted regressions and structural equation models, to evaluate the existence

96 of variations in the sign, strength, and degree of importance of the structural set

97 of relationships identified by previous work on Neotropical dung beetles. Our main

98 hypothesis is that near the tropics water may have greater importance, while

99 temperature and energy will have greater importance in higher latitudes.

100 Following previous results (Chapter 1), we expect that abundance and mammal

101 diversity will not have a strong spatial variance in their relative importance.

102

132

103 Methods 104 Data construction 105 We constructed a database with published data from standardized surveys

106 performed by the authors (Table S2-1). We only used surveys conducted since

107 1999, to match the temporal period of the climate data (see below). In each work,

108 we extracted information about the number of species, their total abundance, the

109 type of habitat (classified in open or closed), type of bait used (classified in

110 omnivorous, herbivorous, rotten fruit and rotten meat), year, and country. In total,

111 we obtained 34 databases, that were separated by transect and type of bait,

112 totaling 583 sites distributed in three large regions of Mesoamerica, the Amazon

113 and the Caribbean, and central and southeastern Brazil (Fig. 3.1). For each site,

114 we performed a survey completeness analysis based on the original trap data,

115 using sample coverage indices (Chao & Jost, 2012). We retained the sites with

116 completeness greater than 0.8, assuming that they represent well surveyed local

117 communities. We standardized the richness of these sites according to their

118 coverage, and use these values for all subsequent analyses. From here on we

119 refer to these coverage-based richness estimates as simply as species richness

120 for simplicity. All these calculations were performed in the r package iNext (Hsieh

121 et al., 2016).

133

122

123 Figure 3.1. Location of the studies used in the analyses.

124 Predictor Variables 125 We extracted climatic and environmental information from well-established

126 databases using QGIS. We used the WorldClim 2.0 database (Fick & Hijmans,

127 2017) in a 30 arcseconds resolution to obtain the climatic data for the last two

128 days. Data on the 19 bioclimatic variables from this database was summarized

129 into two axes through a Principal Components Analysis: one accounting for

130 temperature (Climate1) and other accounting for water-related variables

131 (Climate2). For actual evapotranspiration (AET) we used data from CGIAR

132 (Trabuco & Zomer, 2010) also at a resolution of 30 arcseconds. NASA-MODIS

133 database (Didan, 2015) at a 0.05 degrees resolution was used to get the annual

134 mean Normalized Difference Vegetation Index (NDVI) from the year of the

135 surveys for each site. If a work was performed in more than one year, we

134

136 estimated the average of NDVI of all the years surveyed. Soil structure data was

137 extracted from the World Soil Information database (Hengl et al., 2017). We

138 extracted the data of soil (sand, silt, clay, coarse fragments, and bulk density) at

139 three depths: 0.15m, 0.6m, and 2m in a resolution of 250m. Then we performed

140 a PCA and used the broken stick criterion to choose the axes that are most

141 informative to represent soil structure. Data on the 15 soil structure from this

142 database was summarized into three axes through a Principal Components

143 Analysis: one accounting for sand (Soil1), other accounting for bulk density

144 (Soil2), and other accounting for coarse fragments volume (Soil3).

145 For landscape cover richness and altitude we created a 10km radius buffer

146 around the georeferenced point of each study point. We used the Global Land

147 Cover by National Mapping Organizations GIS database (GLCNMO Version

148 3; Tateishi et al., 2014) at a resolution of 15 arcseconds to calculate the number

149 of different land cover/use categories in the buffer. For altitude, we extracted the

150 buffer maximum and minimum altitude from FAO elevation database (Fischer et

151 al., 2012) at a resolution of 30 arcseconds. The richness of mammals at each

152 survey site was extracted from Biodiversitymapping data (Pimm et al., 2014) at a

153 resolution of 10km, excluding volant mammals. Habitat type was classified into

154 open or closed according to the (typically published) information gathered in the

155 field by each study. When the information available for a particular study did not

156 allow to distinguish which traps pertained to open and close habitats, the site was

157 classified as pertaining to both categories (open and closed). We also identified

158 the type of bait utilized and categorized it into rotten meat, carnivorous dung,

159 herbivorous dung or omnivorous dung. This variable was used as a random effect

135

160 in all models to control for any variance in richness due simply to the bait utilized

161 in the study.

162 Statistical analyses 163 We used a combination of structural equation models (SEM) with geographically-

164 weighted linear mixed models (GWLMM) to evaluate the spatial variation in the

165 strength of the relationships among dung beetle species richness and

166 abundance, and the predictor variables (E.B. Pereira & T.F. Rangel,

167 unpublished). Specifically, we assessed the geographical variability in the

168 hypothesis of the relationships among the determinants of Neotropical dung

169 beetle species richness proposed by Pessoa et al. (Chapter 1 of this thesis).

170 Briefly, we constructed a structural hypothetical model of the relationships

171 between the variables used to account for these hypotheses and dung beetle

172 richness (Fig. 3.2). With the GWLMM we conducted a separate LMM regression

173 for each site, where all data points are weighted according to their geographic

174 distance to the focal site (following Fotheringham et al., 2002). This provides

175 regression statistics for all sites, and thus allows analyzing the variance of the

176 beta score of each relationship throughout space, and also to spatialize the

177 variation in the strength of each relationship among sites. For the GWLMM we

178 used the package rsaeGWR (Baldermann, 2017), following the procedure

179 implemented by E.B. Pereira & T.F. Rangel (unpublished).

180

136

181

182 Figure 3.2. Conceptual Model depicting the hypothesis about the relationships among the factors that affect 183 Dung Beetle local Richness in the Neotropics, following Pessoa et al. (Chapter 1). 184

185 Results 186 Survey completeness was high for most of the sites evaluated. Only 61 sites had

187 completeness scores smaller than 0,8, and most of the remaining 482 well-

188 surveyed sites presented scores higher than 0,9 (Fig. 3.3). The coverage-based

189 estimates of local dung beetle species richness ranged from 1 to 65 species,

190 being the richer sites located in the Flooded Savannas of the Pantanal and the

191 moist forests of the Amazonian basin, presenting greater variation (Fig. 3.4 and

192 3.5).

193

194 Figure 3.3. Frequency of Completeness scores of all sites of the dung beetles local community database 195 considered as well-surveyed (completeness scores higher than 0.8).

196

137

197

198 Figure 3.4: Dung beetle Estimated Richness for each biome surveyed in the database of the Neotropics.

199

200

138

201

202 Figure 3.5. Dung Beetle estimated richness for each surveyed site, the points are jittered in the map. The 203 sizes of the circles represent the completeness of the sites.

204 The main driver of local Scarabaeinae Richness in the Neotropics is

205 abundance (Fig. 3.6A). However, although the preeminence of this effect holds

206 up for the whole realm, the main drivers of abundance (Fig. 3.6B) divide the

207 Neotropics into two regions, one above 10°S, where Climate2 presents the

208 strongest relationship with, and other below this parallel, where Mammal

209 Richness is the main driver. The main drivers for Mammal richness show a higher

210 geographical heterogeneity (Fig. 3.6C) being Climate2 for Mesoamerica region,

211 Soil 3 for the Amazonian region, Climate1 for the sites in the Atlantic Forest, and

212 a mixture of these three factors for the Cerrado biome.

139

213

214 Figure 3.6. Main Drivers of dung beetle species richness in the neotropics. In A main drivers of 215 Scarabaeinae. In B Main drivers of Scarabaeinae abundance. And in C Main drivers of Mammal S. Yellow 216 points represent Abundance, blue points represent Climate2, red points represent Mammal S, green points 217 represent Climate1, and white points represent Soil3 as the main driver. The sites are jittered.

218 The GWLMM structural equation models(Table S2-2) showed clear

219 differences in the hierarchy of the effects between three distinct regions:

220 Mesoamerica (Table S2-4), Amazonia (including the Amazon basin and the

221 Caribbean)(Table S2-3) and Subtropical Formations (Cerrado and Atlantic Forest

222 sites of Central and south-eastern Brazil)(Table S2-5) (Fig. 3.7). In the low

223 latitudes of the Amazonian region, we found strongest positive relations with

224 Scarabaeinae richness of NDVI, while Climate1 (temperature), Mammal richness

225 and Abundance presented relatively lower positive relationships, and both Soil

226 variables and Land Cover richness presented a negative relationship (Fig. 3.8).

140

227 These relationships mostly hold up for in Mesoamerica, although the importance

228 of temperature is higher. Climate1 and Land cover richness presented the highest

229 positive relationships with richness, while the relationship with abundance was

230 weak, though positive, and NDVI, Soil2, Soil3, and Mammal richness presented

231 negative relationships. Strikingly, in the Subtropical Formations Abundance

232 presents the strongest relationship with species richness and, contrary to

233 Mesoamerica, the effect of Climate1 was negative, those of Soil3 and Mammal

234 Richness positive, and the importance of NDVI was weaker than for this latter

235 region. Only Soil2 and Land Cover richness presented similar relationships in

236 both regions.

237

238 Figure 3.7. Summary of the Structural Equation Models hypothesis of Dung Beetle Richness Drivers. Red 239 dots Meso-America region. In Green Amazonian Region. In Blue Subtropical formations. Black lines 240 represent positive relations and red line negative. Dot-dashed lines represent relations where the region.

141

241 presented both relations. diversity, measured as the values of the values as measured diversity, rs of rsof . Spatial variation of the strength of the relationships between dung beetles local richness (Scarab S) and different drive different and S) (Scarab beetleslocal dung richness between relationships ofthe strength ofthe Spatial variation . 8 3.

Figure jittered been has sites the of location The values). black(positive to values) (negative red from goes scale The models. equation structural GWLMM in coefficients betas the nearby the sites. visualizationof allow to

142

242 Regardless of the variations between regions and sites, it is important to

243 note that Abundance, Climate1, and Mammal richness presented positive

244 relationships with dung beetle richness throughout most of the Neotropics, and

245 the effects of soil properties were consistently negative relations in all sites. Only

246 Mammal richness presented an extremely low (-0.02) negative relationship only

247 in Mesoamerica, and Climate1 at the Subtropical formations (-0.03). For variables

248 related to abundance (Fig. 3.9), we found that Climate2 (water) and NDVI

249 presented opposing patterns. North of 10°S latitude Climate has a negative

250 effect, while south of this parallel the effect is positive, and the opposite occurs

251 for NDVI. Interestingly, although Land cover presented positive relationships with

252 Scarabaeinae Richness in some regions, its effect on abundance was

253 consistently negative. Soil2 also presented the opposite effect than for Richness,

254 for its relationship with abundance was in some degree positive in all regions.

255 Mammal richness also showed distinct effects in all three regions, with strong

256 positive relationship in the Amazonian region, weak positive relationship in

257 Mesoamerica and negative relationship in the south.

258

143

259 260 Figure 3.9 Spatial variation of strength relations (betas) of dung beetle abundance (Abundance) with drivers 261 of diversity. The scale goes from red (negative values) to black(positive values). The sites are jittered. 262 Biomes color as previously legend.

263 The indirect effects of Climate1, Climate2, Landcover S and Soil3 trough

264 Mammal richness also presented a pattern of three distinct regions, Meso-

265 America, Amazonian and Subtropical formations (Fig. 3.10). In the Mesoamerica

266 region, only Climate 2 presented positive relationships, Land Cover richness and

144

267 Soil3 presented weak positive relationships, while the effects of Climate1 were

268 negative. For the Amazonian Region, Climate1 and Climate2 present positive

269 relationships, and land Cover richness and Soil3 negative. Interestingly, in the

270 Subtropical Formations, these relationships showed some variations between

271 biomes. Only Land Cover richness showed a consistent positive relationship,

272 while the effects of Climate1, Climate2, and Soil3 shifted between the Cerrado

273 and the Atlantic Forest sites. While in the Cerrado mammal richness was

274 positively related to all three variables, in the Atlantic Forest its relationships with

275 Climate1 and Climate2 were negative.

276 277 Figure 3.10. Spatial variation of strength relations (betas) of Mammal Richness (Mammal S) with drivers of 278 diversity. The scale goes from red (negative values) to black (positive values). The sites are jittered. 279

280

145

281 Discussion 282 The main driver of dung beetles local richness in the Neotropics is abundance.

283 The size of the community consistently mediates its diversity throughout all

284 studied areas. However, the influence of other factors on both abundance and

285 richness varies widely in space. By using an analytical framework that takes the

286 eventual heterogeneity in the relationships between variables across space we

287 were able to identify at least three regions where the local diversity of

288 Scarabaeinae species is driven by distinct ecological dynamics. Dung beetle

289 communities respond differently to climate, soil properties, habitat and mammal

290 richness depending on whether they are located at Mesoamerica, the Amazon

291 (and the Caribbean), or subtropical (Cerrado and Atlantic Forest) regions. This

292 adds an additional dimension to the already complex network of factors identified

293 by Pessoa et al. (Chapter 1), evidencing that the diversity of processes behind

294 species richness patterns is not limited a mere accumulation of concurring

295 effects. The structure of these complex relationships also varies in space.

296 That abundance is the main driver of dung beetle local richness provides

297 support for the importance of the more individuals’ hypothesis (Hutchinson, 1959;

298 Wright, 1983; see also Chapter 1). According to this theory, species diversity is

299 mediated by the amount of energy available in the community through the

300 increase of resources, which in turn is responsible of increasing the number

301 individuals of the populations, diminishing extinction rates and increasing the

302 accumulation of species (Srivastava & Lawton, 1998; Storch et al., 2018). Dung

303 beetle abundance is affected by numerous local-scale processes, including:

304 changes in habitat structure (through fragmentation, e.g. Estrada et al., 1999;

305 Estrada & Coates-Estrada, 2002; alterations, e.g. França et al., 2018; or directly

306 conversion, e.g. Almeida et al., 2011); changing resources (in quality, e.g.

146

307 Gittings & Giller, 1998; Amézquita & Favila, 2010; quantity, e.g. Peck & Howden,

308 1984; or diversity, e.g. Lumaret et al., 1992; Culot et al., 2013); and seasonality

309 (both in temperature, e.g. Agoglitta et al., 2012; and water availability, e.g.

310 Andresen, 2005; de Siqueira Neves, et al., 2010). But the variations in

311 productivity that are central to the mechanism of the More Individuals Hypothesis

312 are determined by water availability (Climate2) in the Amazonian and

313 Mesoamerican communities, and by mammal richness in the Subtropical

314 Formations of the Cerrado and the Atlantic Forest. Both factors are related to the

315 availability and heterogeneity of resources, and thus to the amount of energy and

316 productivity (Ricklefs, 1977). Water availability may be important for dung beetles

317 due to its influence on the humidity of the fecal matter, which increases its

318 palatability and attractiveness, thus enhancing resource utilization (Halffter &

319 Matthews, 1966). Interestingly, the effects of mammal diversity on dung beetle

320 communities in southeastern Brazil seem to be more complex, for it has a direct

321 positive relationship on richness but a negative effect on abundance. This may

322 be because its effect on dung beetle richness is due to the joint evolution of both

323 groups, with increasing dung beetle diversity through niche packing as the variety

324 of resources (i.e. feces of different mammal species) increases (Ahrens et al.,

325 2014). Whereas the effect on abundance may reflect the recent process of

326 homogenization of mammal communities through the increase in cattle ranches,

327 which may increase the amount of resources (i.e. cow dung) under the sake of

328 increasing the dominance of a handful of species that are benefited by the

329 transformation of native ecosystems into pastures for livestock (Nichols et al.,

330 2007; Nichols et al., 2009).

147

331 The geographical heterogeneity in the effects of the different factors –in

332 particular of climate and mammal richness– results in a clear regionalization in

333 the determinants of local Scarabaeinae richness, where at least three distinct we

334 can be identified within the Neotropics. These differences between regions

335 provide support for the claim that many basic ecological processes are

336 determined at the level of regional communities (Ricklefs, 2008). According to the

337 Regional Community Concept, the coexistence of species in local communities

338 is the result of long-term (co)evolutionary processes and historical and ecological

339 factors acting at a regional scale (Ricklefs, 2015). In our study, a number of

340 regional aspects contribute to the different responses of Scarabaeinae

341 communities to the same factors. The Amazonian and Caribbean locations share

342 a common history of species that evolved in humid forests (Hanski & Cambefort,

343 1991) but with a complex mixture of vicariance processes (Antonelli et al., 2009),

344 and higher climatic stability through Pleistocene glaciations (Miller et al., 1993).

345 This contrasts with the dung beetle fauna of Mesoamerica. In this region, most

346 dung beetle lineages are of South American origin and colonized the region when

347 the two Americas united (Davis et al., 2002), to then undergo a process of

348 adaptation to the dryer and more open areas (Halffter, 1991).

349 Perhaps the most heterogeneous responses in one of the three regions

350 we identified are shown by the communities in the subtropical formations. The

351 Cerrado landscapes present open areas, and therefore its dung beetle species

352 pool evolved species adapted to open environments (Davis et al., 2002). In

353 contrast, in the Atlantic forest open areas where restricted to high altitudes, which

354 resulted in that most species in the pool of this biome are adapted to forest

355 environments. But despite the evident ecological differences between the

148

356 Cerrado and the Atlantic Forest, these two biomes present a similar history of

357 contraction and expansion of its area, but in inverse times (i.e., when savannas

358 expanded, Atlantic forest contracted and so on), sharing also many common

359 lineages. Their differences become however important when facing the effects of

360 the recent anthropogenic disturbances. The conversion of forest to pastures and

361 introduction of cattle had less impact in the Cerrado than in the Atlantic forest,

362 because a large number of species already adapted to the open areas of the

363 Brazilian savannas were able to colonize the novel habitats (Correa et al., 2019).

364 It is likely that at least part of the divergence between the responses of dung

365 beetle communities to mammal diversity in these two biomes identified by our

366 analyses is driven by the ability to adapt to the decline in the populations of native

367 mammal forest species by reaching and exploiting the novel cattle dung resource

368 that is abundant in the new pasture habitats (see Chapter 3 of this dissertation).

369 Conclusions 370 The main mechanism behind the diversity of dung beetle communities in the

371 Neotropics is the increase in abundance through greater productivity, in

372 accordance with the More Individuals Hypothesis. The rest of the effects identified

373 by Pessoa et al. (Chapter 1), including climate, soil, habitat and mammal

374 diversity, are also important drivers of Scarabaeinae richness, but their effects

375 are heterogeneous in space. Our analyses identify at least three distinct regional

376 communities (sensu Ricklefs, 2008) which show internally consistent responses

377 to the factors analyzed here. The differences in the responses of local

378 communities between these regional communities may be determined by the

379 combination of the evolution of distinct species pools adapted to different

380 conditions for example, when natural forest areas are cleared for cattle-breeding,

381 the effects of mammal diversity can shift from positive to negative depending on

149

382 the ability of the species pool of each region to exploit the pastures. All in all, our

383 results evidence that local diversity is a complex phenomenon arising from

384 multiple drivers acting simultaneously, and that the ecological, evolutionary and

385 historical differences between regions promote different responses to the same

386 factors.

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1 CAPÍTULO 3 2

4 Capítulo 5 FOREST CONVERSION INTO PASTURE SELECTS 5 DUNG BEETLE TRAITS AT DIFFERENT BIOLOGICAL 6 SCALES DEPENDING ON SPECIES POOL 7 COMPOSITION 8

9 Marcelo Bruno Pessôa¹,*, Paulo De Marco Junior¹ and Joaquín

10 Hortal¹,²

11 1 Departamento de Ecologia, Instituto de Ciências Biológicas, Universidade 12 Federal de Goiás, Avenida Esperança s/n, Campus Samambaia, ICB 5, CEP 13 74690-900, Goiânia, Goiás, Brazil 14 2 Department of Biogeography and Global Change, Museo Nacional de 15 Ciencias Naturales (MNCN-CSIC), Madrid, Spain 16 *Author for correspondence. E-mail: [email protected] 17

18 Abstract: The conversion of forest to open areas has large effects on native 19 communities. By changing habitat structure, excluding species that were present 20 and facilitating invasions, this transformation affects the functional diversity of the 21 community. Dung beetles (Coleoptera: Scarabaeinae) can provide good 22 information about the processes involved in the responses to forest conversion 23 because they present rapid responses to ecological changes and are easy to 24 collect. Considering the uneven temporal pattern of human occupation of Brazil 25 we expect that some differences between biomes appear. The longer since the 26 disturbance occurred, the more time the community had to re-establish in the 27 habitat, so in recent disturbances only a few species would colonize the new 28 habitat, thus diminishing the size and functional diversity of the community. This 29 would result in large differences between the remaining fragments and the new 30 habitat. To assess these effects, we conducted standardized surveys of dung 31 beetle communities in seven forests fragments and adjacent pastures at two 32 different regions pertaining to the Atlantic forest (Itajaí Valley) and the Cerrado 157

33 (Goiânia region) biomes, using pitfall traps baited with human and cow dung, and 34 rotten liver. We measured fourteen traits in individuals collected in each type of 35 habitat at each particular site. We calculated the functional richness, functional 36 evenness, functional divergence and community-weighted mean of traits for each 37 area, and analyzed the individual variation through Trait Statistics. Communities 38 were both richer and more numerous at Goiânia. Functional divergence was 39 larger in this region, as well as in the forest for both regions. We did not find any 40 relationship between the functional diversity indices and forest conversion 41 beyond the effects of richness. Strikingly, the effects of habitat change on trait 42 diversity are dependent on the regional species pool, rather than on time since 43 the conversion of forest into pasture. Although landscape changes were more 44 recent at Goiânia, the functional loss in these communities is lessened by the 45 colonization of the new habitat by species adapted to inhabit open habitats. This 46 contrasts with the Itajaí Valley, where there were no natural open habitats, so 47 pastures are colonized only by generalist forest species and alien invaders, 48 despite the larger time since land conversion. Importantly, the evolutionary 49 differences between regional communities determine the scale at which trait 50 filtering operates. Trait selection occurs at the individual level for some traits in 51 the Cerrado, and at the species level in the Atlantic forest.

52 53

55 Introduction 56 Forest conversion is one of the many threats to biodiversity in tropical landscapes 57 (Newbold et al., 2015). The conversion to open areas has large effects on the 58 native communities, through changes in habitat structure, the exclusion of native 59 species and the facilitation of invasions. Such loss of native species and 60 replacement by aliens may affect ecosystem functioning, reducing the 61 effectiveness of the community in utilizing resources resisting other disturbances 62 (Harrison et al., 2014). Functional diversity provides a mean to assess such 63 effects on the biodiversity-ecosystem functioning relationship (Flynn et al., 2011). 64 Functional diversity is measured from the range of variation in the functional 65 traits of the species that are present in the community, assuming that ecological

158

66 functioning can be indirectly assessed through the diversity of traits with 67 functional meaning. Within this framework, a functional trait is any measurable 68 characteristic (morphological, biochemical, phenological, physiological, and 69 behavioral) of the individual that affects either its fitness (Violle et al., 2007) or 70 the fitness of other individuals of the same or different species. Traits are used 71 under the assumption that these characteristics provide information on the ability 72 of individuals to perform particular functions and/or respond to biological 73 interactions, thus providing a good proxy for ecological functionality. Therefore, 74 by measuring different aspects of trait variation different indices of functional 75 diversity are thought to account for different aspects of functioning (Mason et al., 76 2005): Functional richness measures the functional (i.e. trait) space occupied by 77 the species in the community; Functional Evenness does so for the regularity in 78 the use of this space; and Functional Divergence accounts for how the 79 differences in the distribution of the species in the trait space, which may 80 contribute to a better use of resources. 81 The use of traits in functional ecology is less disseminated –though 82 increasing– for animals than for plants, and has been mostly focused on the study 83 of assembly processes (Moretti et al., 2017). Here it is important to highlight the 84 low rate of experiments assessing trait functionality in animal functional ecology 85 (see Noriega et al., 2018 for insects). Dung beetles (Coleoptera: Scarabaeinae), 86 however, are an exception to this, being one of the groups where experiments 87 had been carried out (e.g. Emlen et al., 2005; Slade et al., 2007). Indeed, dung 88 beetles can inform about the processes involved in the responses to forest 89 conversion. They are a good model for these studies because they present rapid 90 responses to ecological changes, and are easy to collect (Gardner et al., 2008). 91 Dung beetles are well known for their feeding in mammal dung and for the 92 behavior of making and rolling balls shown by some of them (Halffter & Matthews, 93 1966). Their most iconic function is dung removal, but they also provide other

94 functions like parasite and fly control, soil bioturbation, contribute to diminish CO2

95 emission in pastures, incorporate NO3 in the soil, act as secondary dispersal of 96 seeds and enhance plant grow (Nichols et al., 2008; Slade et al., 2016). Their 97 distinct feeding behaviors provide a classification in guilds that provides a rapid 98 approach to their functional diversity (Doube, 1990; see also Pessôa et al., 2017). 99 They can be classified as Rollers, that make a dung ball and roll away; Tunnelers,

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100 that burrow the dung; and Dwellers, that live directly in the dung (Bornemissza, 101 1969). The knowledge of their natural history may help to better interpretations of 102 the patterns observed in nature. 103 In the case of Neotropical dung beetles, the conversion of forest into pasture 104 affects community structure by diminishing the richness and increasing the 105 dominance of a few species (Nichols et al., 2007; Sánchez-de-Jesús et al., 2016). 106 In functional terms, forest conversion affects dung beetle food relocation 107 behavior, body size and daily activity (i.e. diurnal, nocturnal or crepuscular) 108 (Nichols et al., 2013). Functional diversity indices provide some information about 109 these responses. Namely, dung beetle functional richness and divergence 110 diminish as the impact of forest conversion increases (Barragán et al., 2011). 111 Although the effects of land use change on Neotropical dung beetle communities 112 are relatively well known (e.g. (Klein, 1989; Dale et al., 1994; Gardner et al., 2008, 113 Lopes et al., 2011; Korasaki et al., 2013), there is a need for a better 114 understanding of their responses considering their evolution in more forested 115 areas, in comparison to Afrotropical and Palearctic regions. 116 Both Atlantic Rain Forest and Cerrado are distinct biodiversity hotspots, and 117 their biotas are the result of different evolutionary history and processes that, 118 arguably, have resulted in different regional pools of species. In general dung 119 beetle diversity is greater in the forest than open areas in the tropics (Hanski & 120 Cambefort, 1991; but see Silva et al., 2019) and this reflects in a conspicuous 121 difference between Atlantic Forest and Cerrado diversity (Durães et al., 2005), 122 since Cerrado has more natural open areas than Atlantic forest. The history of 123 forest conversion in Brazil is spatially uneven (Leite et al., 2012). The Atlantic 124 Rainforest was one of the first areas to be converted, mostly because it is situated 125 in the coastal region, providing easy access to the European settlers (Dean, 126 1997). The Cerrado (i.e., the Brazilian Savannah), on the other hand, was 127 exploited more intensively in the expansion and internalization of the Brazilian 128 population promoted by President Getulio Vargas in the 1950s (Oliveira & 129 Marquis, 2002). Therefore, we expected that the differences in the functional 130 adaptations evolved at each biome would also have an effect on their ability to 131 colonize the novel open habitats. 132 The aim in this paper is to evaluate the effects of forest conversion into the 133 pasture on the functional diversity of dung beetle communities at the Atlantic

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134 Forest and the Cerrado, two regions with different evolutionary histories. More 135 specifically, we use data on community composition and trait measurements 136 gathered from standardized surveys of forest fragments and pastures from seven 137 landscape within each biome. Then, we evaluate whether forest conversion 138 selects particular functional traits of dung beetles in each region through 139 functional diversity indices and trait variations both between and within species. 140

141 Methods 142 Study areas 143 The study was carried out in two different regions of Brazil, the Itajaí Valley (Santa 144 Catarina), and the surroundings of Goiânia (Goiás) (Fig. 4.1). The Itajaí Valley is 145 characterized by Atlantic Rain Forest formations (Fig. 4.1A). The Goiânia region 146 is located in the Cerrado biome and is characterized by Cerrado (i.e. Brazilian 147 savannah) formations (Fig. 4.1B). The dung beetle species pool in each region 148 was obtained from the results of our surveys (see below). 149

150 151 Figure 5.1: Location of the regions and areas utilized for dung beetle surveys. A Goiânia region 152 – Cerrado. B Itajaí Valey region – Atlantic Forest.

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153 Dung beetle surveys 154 In each region, Cerrado and Atlantic Forest, we selected seven areas separated 155 at least 1km from each other. In each one of those areas, we conducted 156 standardized surveys in two adjacent sites, one of forest and other of pasture. 157 Dung beetle captures were made with baited pitfall traps consisting of 1-liter pots 158 with a solution of water, salt, and detergent. The baits were suspended above the 159 trap with wire in a 50 ml plastic cup (Fig. 4.2A). Three different types of baits were 160 used: human feces, rotten liver, and cow dung. We placed three replicates of 161 each type of bait, so in total nine pitfall traps, spaced 50 m apart, were placed in 162 each sampling site (Fig. 4.2B). The traps remained 48h in both habitats (forest 163 and pasture). We considered each pair of habitats as a sample unity. The surveys 164 occurred in the rainy season of 2016 and 2017. All collected beetles were 165 identified Fernando z. Vaz-de-Mello, and deposited in the UFG entomological 166 collection.

167 168 Figure 5.2: A) Baited Pitfall trap used to collect the dung beetles. B) Trap placement design.

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169 Measuring dung beetle functional traits 170 We compiled information on a set of functional traits for each species and site 171 based on measurements of the beetles collected in the surveys. In total, we 172 selected fourteen traits related with: dispersion (wing load, wing area/length ratio, 173 and eye dorsal area)(Hongo, 2010; Byrne & Dacke, 2011; Dacke et al., 2013), 174 excavation (prosternum height, protibiae area, pronotum width, head length and 175 head width)(Halffter & Matthews, 1966; Vilhelmsen et al., 2010), resource use 176 (body size, measured as pronotum length + elytra length, and volume measured 177 as length * pronotum width * prosternum height)(Andresen, 2003; Emlen et al., 178 2005; Radtke & Williamson, 2005), food reallocation (horizontal displacement 179 and metatibia length)(Halffter & Matthews, 1966), breeding behaviour (nesting 180 habit and nest shape - pear/ball)(Halffter & Matthews, 1966), diel 181 activity(Hernández, 2002), and specialization (food specificity)(Falqueto et al., 182 2005) (Fig. 4.3). 183 The morphological traits were measured in five individuals per species and 184 habitat (forest/pasture) in each area, or in all captured individuals for species with 185 less than five individuals in each of the sites. That is, we measured traits in up to 186 10 individuals per species per area, and up to 70 individuals per species per 187 region. To obtain trait measurements, pictures of each individual were taken, and 188 the traits were measured in the software ImageJ (Rueden et al., 2017). Finally, 189 food specificity was measured using the Levin’s index of niche breadth based on 190 the abundance of individuals of each species in traps with each type of bait of all 191 traps placed in the same region, assuming the wider the niche more generalist is 192 the species.

193 Functional diversity indices 194 Trait measurements were used to calculate three functional diversity indices: 195 Functional Richness (FRich), Functional Evenness (FEve) and Functional 196 Divergence (FDiv). FRich measures the functional space of a community (Mason 197 et al., 2005), and is calculated by the convex hull volume of all the traits of the 198 species present in the community (Villéger et al., 2008). FEve represents the 199 regularity of abundance of the species in the functional space (Mason et al., 200 2005), and is measured by using a minimum spanning tree based on trait the 201 similarity between species or individuals (Villéger et al., 2008). FDiv represents 202 the degree in which the distribution of species in the functional space maximizes

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203 the divergence of traits in the community (Mason et al., 2005), and is calculated 204 by measuring the distance of the species to the centroid of the functional space 205 (Villéger et al., 2008). Further, to assess differences in any trait between the two 206 types of habitats we calculated the community-weighted mean (CWM, Garnier et 207 al., 2004) of each trait for each assemblage.

208 To analyze the effects of habitat on trait variations between and within 209 species we used the decomposition of the variance in nested scales based on 210 restricted maximum likelihood (REML) (Messier et al., 2010) so that we can 211 analyze the biological scale providing greater variance in the traits. Further, we 212 used Trait Statistics (Tstatistics) (Violle et al., 2012) to understand how internal 213 or external filters are acting in the assemblages of both types of habitats.

214 Statistical Analyses 215 We used generalized mixed models to assess the effects of habitat on all 216 functional indices, accounting for area differences by including this factor as a 217 random effect. To assess whether the assembly of trait values in each forest and 218 open location was associated with a selection of certain trait values we measured 219 the departure of each sampling site from a random assembly of the pool of 220 species of each region through Standardized Effect Size (Gotelli & McCabe, 221 2002; Gotelli et al., 2011). 222

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223 224 Figure 5.3. Dung beetle functional traits measured in five individuals per habitat per area. 1. 225 Dorsal Eye Area, 2. Head Length, 3. Head Width, 4. Pronotum Length, 5. Pronotum Width, 6. 226 Elytra Length, 7. Protibia Area, 8. Metatibia Length, 9. Prosternum Height, 10. Wing Area. Body 227 Length was calculated summing Pronotum Length and Elytra Length. Wing load was calculated 228 by the ratio of wing area by body size. And Volume was calculated by multiplying Body Size, 229 Pronotum Width, and Prosternum Height.

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230 Results 231 In total, 2682 individuals were captured by our surveys, 2143 in the Cerrado 232 (Table 4.1, Table S3-1) and 538 in Atlantic Forest (Table 4.2, Table S3-2). These 233 individuals pertaining to a total of 63 species from 18 genera. In Atlantic Forest 234 the most common species were Canthon rutilans cyanescens Harold, 1868 (43% 235 of all captured individuals), Coprophanaeus dardanus (MacLeay, 1819) (15%) 236 and Deltochilum multicolor Balthasar, 1939 (9%); and Canthidium sp.1 (61%), 237 Onthophagus ptox Erichson, 1842 (25%) and Trichillum externepuctatum 238 Preudhomme de Borre, 1880 (23%) were so at the Cerrado. Cerrado was richer, 239 with 42 species in contrast with the 21 species found at Atlantic Forest. Forest 240 habitats hosted more species than pasture in both regions (32 versus 23 species 241 in Cerrado, and 20 versus 6 species at the Atlantic Forest). While in Atlantic 242 Forest only one species was exclusively in the pasture and 15 exclusively in the 243 forest, the Cerrado region presented 11 species exclusive to pastures and 20 244 species exclusive to the forest. Results from a Principal Coordinates Analyses 245 from species abundance data evidence the differentiation in the species pools of 246 both regions (Fig. 4.4). But also, that species composition differs clearly between 247 forests and pastures in Cerrado, while these habitat differences are smaller for 248 the Atlantic Forest since sites from both types of habitats largely overlap in the 249 first two ordination axes

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1 Table 4.5.1. Dung beetle collected in Forest and Pasture in Cerrado.

FOREST PASTURE Total % Total % N N Ateuchus aff. pruneus 3 0.001957 Agamopus viridis 5 0.008197 Ateuchus vividus 1 0.000652 Canthon aff. piluliformis 13 0.021311 Canthon aff. piluliformis 1 0.000652 Canthon curvodilatatus 2 0.003279 Canthidium aff. Lucidum 1 0.000652 Canthon lituratus 138 0.22623 Canthidium sp.1 816 0.53229 Canthon sp. 1 0.001639 Canthidium sp.2 1 0.000652 Canthidium aff. barbacenicum 14 0.022951 Canthidium sp.3 1 0.000652 Canthidium aff. lucidum 1 0.001639 Canthonela sp. 2 0.001305 Canthidium sp.2 1 0.001639 Coprophanaeus cyanescens 27 0.017613 Canthonela sp. 5 0.008197 Coprophanaeus ensifer 2 0.001305 Coprophanaeus spitzi 1 0.001639 Deltochilum enceladus 10 0.006523 Dendropaemon nitidicolis 1 0.001639 Deltochilum sextuberculatum 36 0.023483 Dichotomius aff. carbonarius 1 0.001639 Deltochilum sp. 89 0.058056 Dichotomius aff. zicani 3 0.004918 Dichotomius aff. Carbonarius 22 0.014351 Dichotomius bos 28 0.045902 Dichotomius aff. zicani 19 0.012394 Dichotomius nisus 34 0.055738 Dichotomius angeloi 1 0.000652 Digitontophagus sp. 27 0.044262 Dichotomius bos 1 0.000652 Eutrichillum hirsutum 7 0.011475 Dichotomius cuprinus 4 0.002609 Isocopris inhiatus 1 0.001639 Dichotomius nisus 1 0.000652 Ontophagus buculus 24 0.039344 Dichotomius sp.1 3 0.001957 Onthophagus ptox 1 0.001639 Dichotomius sp.2 1 0.000652 Trichillum adjuntum 5 0.008197 Dichotomius transiens 6 0.003914 Trichillum externepunctatum 296 0.485246 Eurysternus caribaeus 47 0.030659 Trichillum heydeni 1 0.001639 Eurysternus nigrovirens 20 0.013046 Eutrichillum hirsutum 12 0.007828

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Ontophagus buculus 4 0.002609 Onthophagus ptox 326 0.212655 Ontherus appendiculatus 1 0.000652 Ontherus asteca 2 0.001305 Trichillum adjuntum 1 0.000652 Trichillum externepunctatum 15 0.009785 Uroxys aff. epipleurysternusalis 57 0.037182

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2 Table 4.2 Dung beetle collected in Forest and Pasture in Atlantic Forest.

Forest Pasture Total % Total % N N Canthon aff. luctuosos 1 0.002045 Canthon conformis 2 0.040816 Canthon coloratus 1 0.002045 Canthon podagricus 4 0.081633 Canthon podagricus 1 0.002045 Canthon rutilans cyanescens 4 0.081633 Canthon rutilans cyanescens 229 0.468303 Coprophanaeus dardanus 8 0.163265 Canthidium aff. trinodosum 6 0.01227 Deltochilum multicolor 26 0.530612 Coprophanaeus bellicosus 1 0.002045 Eurysternus paralelus 5 0.102041 Coprophanaeus cerberus 1 0.002045 Coprophanaeus dardanus 76 0.155419 Coprophanaeus saphirinus 10 0.02045 Deltochilum brasilense 13 0.026585 Deltochilum furcatum 28 0.05726 Deltochilum morbilossum 11 0.022495 Deltochilum multicolor 26 0.05317 Dichotomius ascanius 12 0.02454 Dichotomius mormom 9 0.018405 Dichotomius quadrinodosus 1 0.002045 Dichotomius sericeus 20 0.0409 Eurysternus paralelus 19 0.038855 Onthophagus aff. hematopus 20 0.0409 Phanaeus splendidulus 4 0.00818

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250

251

252 Figure 55.4. Pcoa Axis for the dung beetles surveyed in the Cerrado and Atlantic Forest 253 regions. Circles represent Cerrado, and triangles represent Atlantic Forest. In red Forest and in 254 Green Pasture.

255 Functional richness was smaller in Cerrado than in Atlantic Forest (mean of 256 0.0005 versus 0.016), while Functional evenness and divergence were higher in 257 the former region (means of 0.58 versus 0.44 and 0.76 versus 0.49, respectively) 258 (Fig. 4.5). Both regions and habitats had a significant effect only on Functional 259 divergence (t=-2.2, df=12, p=0.048); this index was higher in Cerrado region and 260 in the forest of both regions (Table 1). After removing the effects of richness and 261 abundance through by calculating the Standardized Effect Size none of the 262 indices was related to forest conversion (Fig. 4.6; Table 4.1).

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263 264 Figure 5.5. Dung beetle species richness and functional diversity in forest and pasture habitats 265 obtained in the surveys of Cerrado and Atlantic Forest Regions. Box plots show the average and 266 interquartile range of site values; dots identify extreme values. 267 Table 4.3. Results of the linear mixed models for the effects of habitat and region (and their 268 interaction) on species richness and functional diversity indices, and their standardized effect 269 sizes (SES). S stands for species richness, FRich for functional richness, FEve for functional 270 evenness and FDiv for functional divergence.

Model Value Std.Error DF t-value p-value Habitat -4.429 1.823 12 -2.429 0.0318 S Region -2.857 1.823 12 -1.567 0.143 Habitat*Region -2.714 2.578 12 -1.053 0.3131 Habitat 0.001 0.099 12 0.010 0.9918 Frich Region 0.000 0.099 12 0.000 1 Habitat*Region 0.260 0.141 12 1.848 0.0894 Habitat -0.058 0.130 12 -0.446 0.6638 Feve Region 0.029 0.138 12 0.210 0.8369 Habitat*Region -0.346 0.183 12 -1.889 0.0832 Habitat -0.132 0.144 12 -0.920 0.3759 Fdiv Region -0.043 0.146 12 -0.295 0.7729 Habitat*Region -0.447 0.203 12 -2.200 0.0481 Habitat -0.737 0.324 6 -2.277 0.0631 SESFRich Region -0.489 0.611 12 -0.800 0.4391 Habitat*Region 0.023 0.630 6 0.036 0.9724 Habitat 0.561 0.489 6 1.146 0.2953 SESFEve Region 0.060 0.483 12 0.125 0.9028 Habitat*Region 0.407 0.866 6 0.470 0.6549 Habitat 0.553 0.455 6 1.214 0.2702 SESFDiv Region 0.726 0.437 12 1.660 0.1228 Habitat*Region -1.638 0.798 6 -2.051 0.0861

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271 The analyses on individual traits show that in most cases there was a 272 difference between regions, with the Atlantic Forest (Table S3-4) presenting 273 larger values and greater variance in the community-weighted mean for all 274 continuous traits (Table 4-2; Fig. 4.7, Supplementary Figure S3.1). In contrast, 275 habitat only showed significant or nearly significant effects on eye dorsal area 276 and metatibia length. Indeed, habitat contributed little to the nested variance of 277 traits, and the differences between species were the principal factor that 278 promoted variance in both regions (Fig. 4.8). However, Prosternum Height, 279 Protibia Area and Eye Dorsal Area showed greater intraspecific variance in 280 Cerrado (Table S3-3, Supplementary Figure S3.1), indicating that in this region 281 individuals within the same species are filtered between forest and pasture 282 habitats according to these traits. These results are partly corroborated by the 283 Trait statistics, for we found that in Cerrado all traits present negative internal 284 filtering processes except Prosternum Height and Protibia Area in the forest and 285 Eye Dorsal Area, that was so in the pasture, and Wing Load,which presented 286 positive internal filtering in the forest (Fig. 4.9). In this region, the external filtering 287 of individuals was positive for Length, Volume and Metatibia Length in the forest, 288 and negative in the pasture for the same traits plus Pronotum Width. In Atlantic 289 Forest all traits presented negative internal filtering, and the external filtering on 290 individuals was present on Wing Load, – positive in the forest and negative in the 291 pasture, and on Metatibia Length, which was negative only in the pasture (Fig. 292 4.9).

293

294

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295 296 Figure 5.6. Standardized effect size for the different regions and habitats. Circles are Cerrado, 297 triangles Atlantic Forest. Green pastures, Red forests. The scales of the symbols represent the 298 observed value of the index.

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299 300 Figure 5.7. Pcoa Axis for the CWM of dung beetles surveyed in the Cerrado and Atlantic Forest 301 regions. Circles represent Cerrado, and triangles represent Atlantic Forest. In red Forest and in 302 Green Pasture. 303 Table 4.4. Results of the linear mixed models of the Community Weighted Mean of individual 304 traits. Model Value Std.Error DF t-value p-value Habitat 1.529 0.861 9 1.775 0.110 Wing Load Region 2.034 1.010 12 2.014 0.067 Habitat*Region -1.014 1.452 9 -0.698 0.503 Habitat 0.522 0.247 9 2.115 0.064 Eye Dorsal Area Region 0.286 0.267 12 1.073 0.305 Habitat*Region -0.554 0.409 9 -1.354 0.209 Habitat 0.775 0.626 9 1.238 0.247 Prosternum Region 2.154 0.895 12 2.406 0.033 Height Habitat*Region -0.291 1.085 9 -0.268 0.795 Habitat 1.633 1.132 9 1.443 0.183 Protibia Area Region 2.845 1.270 12 2.240 0.045 Habitat*Region -1.237 1.892 9 -0.654 0.530 Habitat 1.338 0.928 9 1.441 0.184 Pronotum Region 4.734 1.280 12 3.700 0.003 Width Habitat*Region -1.104 1.602 9 -0.689 0.508 Habitat 0.595 0.435 9 1.368 0.204 Head Length Region 1.282 0.511 12 2.507 0.028 Habitat*Region -0.601 0.734 9 -0.820 0.434 Habitat 0.834 0.572 9 1.457 0.179 Head Width Region 2.654 0.777 12 3.413 0.005 Habitat*Region -0.758 0.986 9 -0.769 0.462 Habitat 0.870 1.082 9 0.804 0.442 Body Size Region 6.980 1.730 12 4.035 0.002 Habitat*Region -2.212 1.896 9 -1.166 0.273 Habitat 298.558 226.849 9 1.316 0.221 Volume Region 641.515 302.945 12 2.118 0.056 Habitat*Region -220.487 389.963 9 -0.565 0.586 Habitat 0.130 0.241 9 0.538 0.604 Metatibia Region 2.857 0.509 12 5.617 0.000 Length Habitat*Region -0.977 0.430 9 -2.269 0.049

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305 306 Figure 5.8. Nested partition of dung beetle traits variance surveyed in forest patches and pastures 307 in Atlantic Forest and Cerrado. Vol = Volume, Len = Length, W.Lo = Wing Load, Ps.H = 308 Prosternum Height, Me.L = Metatibia Length, Pt.A = Protibia Area, Ey.A = Eye Dorsal Area, He.W 309 = Head Width, He.L = Head Length, Pr.W = Pronotum Width.

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310 311 Figure 5.9. Standardized effect size of Traits Statistics obtained for each dung beetle traits in 312 Cerrado and Atlantic Forest for Forest patches and pasture. Asterisc represent significative 313 values, blue=negative and orange=positive.Vol = Volume, Len = Length, W.Lo = Wing Load, Ps.H 314 = Prosternum Height, Me.L = Metatibia Length, Pt.A = Protibia Area, Ey.A = Eye Dorsal Area, 315 He.W = Head Width, He.L = Head Length, Pr.W = Pronotum Width. T_IP.IC=Internal Filtering of 316 Individuals, T_IC.IR=External Filtering of individuals, T_PC.PR=External Filtering of Species.

317 Discussion 318 Our results show that the effects of the conversion of forest habitats into pastures 319 are dependent on the regional species pool presented. Although in both regions 320 the novel pastures are poorer in species and host individuals with different traits 321 than the native forest habitats, these effects occur at different biological scales in 322 each biome. It could be expected that the more recent conversion at Cerrado 323 would have resulted in poorer pasture communities than in Atlantic Forest, where 324 dung beetles have had a long time for colonizing the novel habitat. However, the 325 pattern is the opposite: Atlantic forest pasture assemblages are poor and 326 dominated by alien species, whereas a large number of native species have been 327 able to colonize the new open areas at the Cerrado. The analyses of trait variation 328 provide a mechanism that may account for these different responses. Habitat 329 filters a large number of traits in both regions. However, in the Cerrado this 330 filtering occurs within species for certain traits, evidencing that the novel habitat 331 is colonized by certain individuals of species that were already adapted to utilize

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332 the (semi)open habitats of the Brazilian savannah. In contrast, in the Atlantic 333 Forest in Itajay Valley, where there were no natural open habitats, such filtering 334 occurs at the species level, so the only exclusive pasture species was rare and 335 the pasture is currently used only by a handful of generalist Atlantic forest species 336 and invaders. These differences in the effect of, arguably, the same habitat 337 filtering are the consequence of the basic evolutionary differences of the two 338 regional communities (sensu Ricklefs, 2015). The more open Cerrado biome has 339 promoted the evolution of species with a larger variance in traits related with 340 digging and flight (i.e. Prosternum Height and Protibia Area, and Eye Dorsal Area 341 and Wing Load, respectively), to accommodate the higher heterogeneity of this 342 biome compared to the more closed and homogeneous Atlantic forest.

343 These regional differences reflect directly on the functional diversity of dung 344 beetle communities. Models on functional diversity indices show strong effects of 345 the interaction between region and habitat, where the pastures always present 346 lower values for the functional diversity indices than the forest, but this effect is 347 stronger in Atlantic Forest than in Cerrado. When the differences due to richness 348 are removed (i.e. by using the Standardized Effect Size; Gotelli & McCabe, 2002), 349 functional differences between both types of habitats come mostly from the 350 loss/substitution of species. Such lower functional diversity of pastures has also 351 been found at the Argentinian Atlantic Forest (Gómez-Cifuentes et al., 2017) and 352 other Neotropical biomes (e.g. Mexican rainforest - Barragán et al., 2011; El 353 Salvador tropical dry forest – Horgan, 2008; Brazilian Pantanal – Pessôa, 2013). 354 This loss of functionality may be an effect of the loss of resource diversity 355 (Lumaret et al., 1992) and/or of the changes in microclimatic conditions (Gómez 356 et al., 2018). Indeed, the lower functional divergence in the pasture may represent 357 a loss in the ability to use different resources (Mason et al., 2005). In any case, 358 the differences between biomes due to differences in their species pools are 359 apparent beyond the raw effects of habitat change. In biomes without open 360 habitat species, pastures are colonized by generalist forest species or by exotic 361 species from other regional pools, such as in the Atlantic Forest example shown 362 here, or the pastures in Amazonian regions that are colonized mostly by Cerrado 363 and Chaco species (Silva et al., 2014).

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364 This difference between open habitat species and forest species is 365 observed in the effects on trait community weighted means. While almost all traits 366 present only a region effect – evidencing the differences in the functional 367 solutions present in the pool of each biome, a handful of traits present significant 368 differences between habitats. Metatibia Length presented differences in both 369 region and in habitat, showing that the effects of habitat filtering on this trait are 370 different in each biome. Cerrado communities were characterized by more 371 dwellers and smaller species than the Atlantic Forest. However, Cerrado 372 presented no differences in CWM between habitats, though presenting a greater 373 variance in the forest probably because of the highest number of rollers and 374 bigger species than the pasture. In Atlantic Forest pasture communities 375 presented smaller metatibia length, because the generalist species that dominate 376 the open habitat are smaller, even despite the dominance of roller species. In 377 Cerrado, we also found an intraspecific variance in the Prosternum Height and 378 Protibia Area. Both traits are related to excavation (Halffter & Matthews, 1966). 379 Indeed, soil texture and compaction affect the assembly of dung beetle 380 communities (Davis, 1996). Therefore, the uneven compaction of the soil in the 381 pasture may be selecting a larger interspecific variance in these traits, through 382 the selection of individuals adapted to exploit soils both well-developed soils and 383 those that have been compacted by cattle.

384 Eye Dorsal Area also provides information on the responses of dung beetle 385 communities to forest clearance. While in both regions this trait presented low 386 CWM and marginal differences between habitats, it presented a greater variance 387 in the pasture of Cerrado. According to the nested decomposition of variance, 388 this higher variability comes mostly from intraspecific variation. This may be due 389 to the presence of species adapted to open areas suffering filtering of individuals 390 with certain trait characteristics, thus increasing the phenotypic diversity of this 391 trait within species. Eye dorsal area can be related to both flight ability, a period 392 of daily activity and the adaptation to different light conditions (Byrne & Dacke, 393 2011). Indeed, the pasture presents a greater influence of light than forests, which 394 can present greater differences in the eye structure of diurnal and nocturnal 395 species. Interestingly, while internal filtering had a negative effect on all traits for 396 which it was significant – thus evidencing a functional clustering within the

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397 species, in the case of Wing Load in Cerrado’s forest such filtering was positive. 398 This means that this trait presents overdispersion within the species in this 399 habitat, where either limiting similarity processes (MacArthur & Levins, 1967) or 400 the high structural complexity may have enhanced the diversification of flight 401 strategies.

402 The greater promoter of individual variance is external filtering. A large 403 number of traits show a signal of external filtering, presenting opposing patterns 404 in the two habitats. While the external filtering processes of the forest promoted 405 overdispersion, in the pasture they promoted clustering. In the pasture, the 406 fluctuation of heat and humidity may impose an important filter of selecting 407 species and individuals with particular trait values. While in the forest the greater 408 environmental stability promotes heterogeneity in the traits and the persistence 409 of more strategies for resource utilization. At the Cerrado forest habitats increase 410 the individual variation of Length, Volume, and Metatibia Length, while in the 411 pasture the individual variation in those traits and Pronotum Width decreases. In 412 the case of Metatibia Length, a trait related with the ability to roll dung balls 413 (Halffter & Matthews, 1966; Hanski & Cambefort, 1991), this effect may be due 414 by the lower presence of rollers in the forest (Krell et al., 2003). The dominance 415 of tunnelers and dwellers in the forest may increase the individual variation in the 416 trait, in contrast with the dominance of rollers in the pasture. Length, Volume, and 417 Pronotum width represent different aspects of body size. Individual variation in 418 size may determine the amount of resource utilized for development (Emlen et 419 al., 2005). So, the greater variation in the forest may be a reflection of the uneven 420 availability of resources in contrast with the greater presence of cow dung in the 421 pasture.

422 Conclusion 423 Forest conversion into pasture impoverishes the diversity of dung beetle 424 communities of the Cerrado and the Atlantic forest. However, the species 425 available in the pool of each biome may diminish this effect, since the ability to 426 colonize the novel habitat depends on the presence of species either previously 427 adapted to this environment, or showing larger phenotypic plasticity. In regions 428 where the pool of species is poor in open area species, time since the land 429 clearance is not important for dung beetle community regeneration. Importantly,

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430 trait filtering occurs independently of the presence of species previously adapted 431 to the new environment. Rather, the evolutionary differences between regional 432 communities determine that this filtering occurs at different scales, at the 433 individual level for some traits in the Cerrado, and typically at the species level in 434 the Atlantic forest.

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187

1 General Conclusions 2

3 Diversity is a complex phenomenon that needs to be studied accordingly.

4 By using a multi-hypothesis approach, we were able to better understand many

5 of the aspects behind the local diversity of dung beetle communities. This allowed

6 us to identify that dung beetle richness is the result of productivity –through the

7 effects of water and energy, and heterogeneity –of both resources and habitats.

8 With an understanding of the factors that better explain dung beetle

9 richness, we can construct complex hypotheses that account for the relationships

10 between these drivers. This approach ultimately allows to interpret which

11 mechanisms are behind dung beetle richness. We found that the "more-

12 individuals hypothesis" is the main driver of local dung beetle richness, through

13 the strength and importance of the effect of Abundance on dung beetle richness.

14 Indeed, most of the effects that local factors such as habitat type and soil

15 structure have on species richness are mediated by abundance.

16 The importance of abundance for dung beetle richness is common for the

17 whole of the Neotropics, but the factors affecting abundance vary between

18 regions. Indeed, species richness presents heterogeneous responses to other

19 predictors throughout this biogeographical region, allowing to identify, at least,

20 three distinct regions: Meso-America, Amazonia and Subtropical formations. The

21 differences in the historical and evolutionary events affecting these three different

22 regional communities may be behind this heterogeneity in the responses of dung

23 beetle communities to the environment.

24 Mammal diversity shows opposite effects on dung beetle richness and

25 abundance; while it correlates positively with richness, its relationship with

188

26 abundance is negative in the regions where mammal diversity is the principal

27 driver of abundance. This may be because the Cerrado and the Atlantic Forest

28 present different species pools, at least in part because of the ling-term presence

29 of open areas in the former biome. In the evolutionary history of this savannah

30 biome mammal diversity acted as a diversifier through niche packing, increasing

31 the heterogeneity of the habitat and food resources available for dung beetles.

32 Due to this, the effects of the recent human colonization and conversion of forest

33 into pasture resulted in different responses. While the Cerrado already hosted a

34 pool of species adapted to inhabit open areas species that were able to colonized

35 the new pasture habitats, this did not happen in the Atlantic forest.

36 This habitat conversion affects also the functional aspect of diversity. And

37 this difference in the species pool make that time itself would not be enough to

38 diminish the effects of this conversion. Without species to colonize the new

39 habitat only generalist or invasive species will be present in the pasture. And this

40 will affect the functional structure of the community. Also, when analyzing the

41 individual variability of traits, we observed that while in the Atlantic forest we have

42 a filtering at species level, some traits in Brazilian Savannah are filtered at

43 individuals’ level.

44 For future works with diversity patterns we suggest to use not only multi-

45 hypothesis approach, but also to approaches that take in account the spatial

46 variance in the drivers of diversity. For Dung Beetle, would be interesting to

47 understand the importance of historical and evolutionary events that may have

48 filtered not only the species, but the traits of individuals.

189

50 Conclusão Geral 51

52 A diversidade é um fenômeno complexo que precisa ser estudado de

53 acordo. Usando uma abordagem multi-hipótese, fomos capazes de entender

54 melhor sobre os aspectos por trás da diversidade local de comunidades de

55 besouros rola-bostas. Isso nos permitiu identificar que a riqueza de besouros é

56 resultado da produtividade, através dos efeitos da água e energia, e

57 heterogeneidade de recursos e habitats.

58 Com uma compreensão dos fatores que melhor explicam a riqueza de

59 besouros rola-bostas, podemos construir hipóteses complexas que explicam as

60 relações entre esses fatores. Esta abordagem permite, em última instância,

61 interpretar quais mecanismos estão por trás da riqueza de escaravelhos.

62 Descobrimos que a "hipótese de mais indivíduos" é o principal fator da riqueza

63 local de besouros de esterco, observado através da força e importância do efeito

64 da Abundância na riqueza de Scarabaeinae. De fato, a maioria dos efeitos que

65 fatores locais, como o tipo de habitat e a estrutura do solo, têm sobre a riqueza

66 de espécies é mediada pela abundância.

67 A importância da abundância para a riqueza de besouros é comum em

68 todo o Neotrópico, mas os fatores que afetam a abundância variam entre as

69 regiões. De fato, a riqueza de espécies apresenta respostas heterogêneas a

70 outros preditores ao longo desta região biogeográfica, permitindo identificar, no

71 mínimo, três regiões distintas: Mesoamérica, Amazônia e formações

72 subtropicais. As diferenças nos eventos históricos e evolutivos que afetam essas

73 três diferentes comunidades regionais podem estar por trás dessa

190

74 heterogeneidade nas respostas das comunidades de besouros de esterco ao

75 meio ambiente.

76 A diversidade de mamíferos mostra efeitos opostos na riqueza e

77 abundância dos besouros rola-bostas; enquanto se correlaciona positivamente

78 com a riqueza, sua relação com a abundância é negativa nas regiões onde a

79 diversidade de mamíferos é o principal fator de abundância. Isso pode ser porque

80 o Cerrado e a Mata Atlântica apresentam diferentes grupos de espécies, pelo

81 menos em parte devido à presença de áreas abertas no antigo bioma. Na história

82 evolutiva desse bioma de savana, a diversidade de mamíferos atuou como um

83 diversificador por meio do empacotamento de nicho, aumentando a

84 heterogeneidade do hábitat e dos recursos alimentares disponíveis para os

85 escaravelhos. Devido a isso, os efeitos da recente colonização e conversão

86 humana da floresta em pastagem resultaram em diferentes respostas. Embora

87 o Cerrado já abrigasse um conjunto de espécies adaptadas para habitar áreas

88 abertas que conseguissem colonizar os novos habitats de pastagem, isso não

89 aconteceu na Mata Atlântica.

90 Esta conversão de habitat afeta também o aspecto funcional da

91 diversidade. E essa diferença no pool de espécies faz com que o tempo não seja

92 suficiente para diminuir os efeitos dessa conversão. Sem espécies para colonizar

93 o novo habitat, apenas espécies generalistas ou invasoras estarão presentes no

94 pasto. E isso afetará a estrutura funcional da comunidade. Além disso, ao

95 analisar a variabilidade individual das características, observamos que, enquanto

96 na Mata Atlântica temos uma filtragem no nível das espécies, algumas

97 características da Savana brasileira são filtradas no nível dos indivíduos.

191

98 Para trabalhos futuros com padrões de diversidade, sugerimos usar não

99 apenas uma abordagem multi-hipóteses, mas também abordagens que levem

100 em conta a variação espacial nos fatores da diversidade. Para rola-bostas, seria

101 interessante entender a importância de eventos históricos e evolutivos que

102 possam ter filtrado não apenas as espécies, mas também os traços dos

103 indivíduos.

192

1 Capítulo 1 Supplementary Information S1

2 Tabela 1-1. Dung beetles studies consulted to construct the database. Title Authors Year Comunidad De Escarabajos Copronecrófagos (Coleoptera: Damborsky et al. 2008 Scarabaeidae) En Dos Bosques Del Chaco Oriental Húmedo, Argentina Escarabajos Copronecrófagos (Scarabaeidae: Scarabaeinae) De La Ibarra-Polesel et al. 2015 Reservanatural Educativa Colonia Benítez, Chaco, Argentina Quantifying Edge Effects: The Role Of Habitat Contrast And Species Peyras et al. 2013 Specialization Spatial And Temporal Variation Of Dung Beetle Assemblages In A Damborsky et al. 2015 Fragmented Landscape At Eastern Humid Chaco Dung Beetle Assemblage In A Protected Area Of Belize: A Study On Latha et al. 2015 The Consequence Of Forest Fragmentation And Isolation Composition And Species Richness Of A Dung Beetle (Coleoptera: Hamel-Leigue et al. 2008 Scarabaeinae) Community In The Lower Yungas Of Cordillera Mosetenes, Bolivia Dung Beetle (Coleoptera:Scarabaeidae) Active In Patchy Forest And Kirk 1992 Pasture Habitats In Santa Cruz Province, Bolivia, During Spring Rapid Turnover And Edge Effects In Dung Beetle Assemblages Ayzama 2003 (Scarabaeidae) At A Bolivian Neotropical Forest-Savanna Ecotone Dung Beetles In Central Amazonian Rainforest And Their Ecological Andressen 2002 Role As Secundary Diperser Effect Of Forest Fragmentation On Dung Beetle Communities And Andressen 2003 Functional Consequences For Plant Regeneration Dung Beetles And Long-Term Habitat Fragmentation In Alter Do Chão, Vulinec et al. 2008 Amazônia, Brazil

193

Effects Of Forest Fragmentation On Dung And Carrion Beetle Klein 1989 Communities In Central Amazonia Rapid Recovery Of Dung Beetle Communities Following Habitat Quintero & Roslin 2005 Fragmentation In Central Amazonia Abundance And Diversity Of Coprophagous Beetles (Coleoptera: Abot et al. 2012 Scarabaeidae) Caught With A Light Trap In A Pasture Area Of The Brazilian Cerrado Improving The Design And Management Of Forest Strips In Human- Barlow et al 2010 Dominated Tropical Landscapes: A Field Test On Amazonian Dung Beetles Habitat Fragmentation Alters The Structure Of Dung Beetle Filgueiras et al. 2011 Communities In The Atlantic Forest Dung Beetles (Coleoptera: Scarabaeidae: Scarabaeinae) Of The Vieira & Silva 2012 Floresta Nacional Contendas Do Sincorá, Bahia, Brazil Resource Utilization And Temporal Segregation Of Scarabaeinae Medina & Lopes 2014 (Coleoptera, Scarabaeidae) Community In A Caatinga Fragment Técnicas De Coleta De Besouros Copronecrófagos No Cerrado Milhomen et al. 2003 New Records Of Scarabaeinae (Coleoptera: Scarabaeidae) In A Matavelli et al. 2013 Biogeographical Transition Zone In The State Of Maranhão, Brazil Dung Beetle (Coleoptera: Scarabaeidae) Assemblages Across A Durães et al. 2005 Natural Forest-Cerrado Ecotone In Minas Gerais, Brazil Utilisation Of Introduced Brazilian Pastures Ecosystems By Native Louzada et al. 2008 Dung Beetles: Diversity Patterns And Resource Use Estrutura Da Comunidade De Scarabaeinae (Scarabaeidae: Coleoptera) Almeida & Louzada 2009 Em Fitofi Sionomias Do Cerrado E Sua Importância Para A Conservação Scarabaeidae E Aphodiidae Coprófagos Em Pastagens Cultivadas Em Koller et al. 2007 Área Do Cerrado Sul-Mato-Grossense Dung Beetles (Coleoptera: Scarabaeoidea) In Three Landscapes In Rodrigues et al. 2013 Mato Grosso Do Sul, Brazil

194

Diversity Of Coprophagous Scarab Beetles (Coleoptera, Scarabaeidae) Rodrigues et al. 2010 Collected With Flight Intercept Trap In The Southern Pantanal, Brazil. Ocorrência E Sazonalidade De Besouros Copro/Necrófagos Koller et al. 1997 (Coleoptera; Scarabaeidae), Em Massas Fecais De Bovinos, Na Região De Cerrados Do Mato Grosso Do Sul Dung Beetles (Coleoptera: Scarabaeidae) Attracted To Dung Of The Pucker et al. 2013 Largest Herbivorous Rodent On Earth: A Comparison With Human Feces Dung Beetles (Coleoptera: Scarabaeidae) Attracted To Dung Of The Pucker et al. 2013 Largest Herbivorous Rodent On Earth: A Comparison With Human Feces Dung Beetle (Coleoptera: Scarabaeidae) Diversity And Community Scheffler 2005 Structure Across Three Disturbance Regimes In Eastern Amazonia Response Of A Dung Beetle Assemblage Along A Reforestation Hernandéz et al. 2014 Gradient In Restinga Forest Diversidade De Scarabaeidae S. Str. (Coleoptera) Da Reserva Biológica Endres et al. 2007 Guaribas, Mamanguape, Paraíba, Brasil: Uma Comparação Entre Mata Atlântica E Tabuleiro Nordestino Scarabaeidae (Coleoptera) Do Parque Estadual Mata Dos Godoy E De Medri & Lopes 2001 Área De Pastagem, No Norte Do Paraná, Brasil Changes In The Dung Beetle Community In Response To Restinga Costa et al. 2014 Forest Degradation Dominant Dung Beetle (Coleoptera: Scarabaeidae: Scarabaeinae) Salomão et al. 2014 Species Exhibit Wider Trophic Niches On Fruits, Excrement, And Carrion In Atlantic Forest, Brazil How Habitat Change And Rainfall Affect Dung Beetle Diversity In Liberal et al. 2011 Caatinga, A Brazilian Semi-Arid Ecosystem Study Of The Dung Beetle (Coleoptera: Scarabaeidae) Community At Silva et al. 2010 Two Sites: Atlantic Forest And Clear-Cut, Pernambuco, Brazil

195

A Comparison Of Dung Beetle Assemblage Structure (Coleoptera: Lopes et al. 2011 Scarabaeidae: Scarabaeinae) Between An Atlantic Forest Fragment And Adjacent Abandoned Pasture In Paraná, Brazil What Is The Importance Of Open Habitat In A Predominantly Closed Costa et al. 2013 Forest Area To The Dung Beetle (Coleoptera, Scarabaeinae) Assemblage First Report On Dung Beetles In Intra-Amazonian Savannahs In França et al. 2016 Roraima, Brazil Escarabeíneos (Coleoptera: Scarabaeidae: Scarabaeinae) De Uma Área Silva et al. 2012 De Campo Nativo No Bioma Pampa, Rio Grande Do Sul, Brasil Attractiveness Of Different Bait To The Scarabaeinae (Coleoptera: Silva et al. 2011 Scarabaeidae) In Forest Fragments In Extreme Southern Brazil Escarabeíneos Copro-Necrófagos (Coleoptera, Scarabaeidae, Silva et al. 2012 Scarabaeinae) De Fragmentos De Mata Atlântica Em Silveira Martins, Rio Grande Do Sul, Brasil Dung Beetle Communities As Biological Indicators Of Riparian Viegas et al. 2014 Forestwidths In Southern Brazil Spatial Variation Of Dung Beetle Assemblages Associated With Forest Silva & Hernandéz 2015 Structure In Remnants Of Southern Brazilian Atlantic Forest Changes In The Dynamics Of Functional Groups In Communities Of Campos & Hernandéz 2014 Dung Beetles In Atlantic Forest Fragments Adjacent To Transgenic Maize Crops Attractiveness Of Native Mammal’s Feces Of Different Trophic Guilds Bogoni & Hernandéz 2014 To Dung Beetles (Coleoptera: Scarabaeinae) The Importance Of Maize Management On Dung Beetle Communities In Campos & Hernandéz 2015 Atlantic Forest Fragments Influence Of Carrion Smell And Rebaiting Time On The Efficiency Of Flechtmann et al. 2008 Pitfall Traps To Dung Beetle Sampling

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Seasonal And Spatial Species Richness Variation Of Dung Beetle Hernandéz & Vaz De Mello 2009 (Coleoptera, Scarabaeidae S. Str.) In The Atlantic Forest Of Southeastern Brazil Coleoptera Associated With Undisturbed Cow Pats In Pastures In Mendes & Linhares 2006 Southeastern Brazil Selective Defaunation Affects Dung Beetle Communities In Continuous Culot et al. 2013 Atlantic Rainforest Species Composition And Functional Guilds Of Dung Beetles (Insecta: Daniel et al. 2014 Coleoptera: Scarabaeidae: Scarabaeinae) In Different Vegetational Types In The Brazilian Shield–Chacoan Depression Border Estrutura Da Comunidade De Scarabaeinae (Coleoptera: Scarabaeidae) Marcelino 2015 Em Três Ambientes De Caatinga Dung Beetle Persistence In Human-Modified Landscapes:Combining Filgueiras et al. 2016 Indicator Species With Anthropogenicland Use And Fragmentation- Related Effects Dung Beetles (Coleoptera, Scarabaeinae) Attracted To Sheep Dung In Correa et al. 2013 Exotic Pastures Environmental Influence On Coprophagous Scarabaeidae (Insecta, Tissinani et al. 2015 Coleoptera) Assemblages In The Pantanal Of Mato Grosso Evaluating The Impacts And Conservation Value Of Exotic And Native Gries et al. 2011 Tree Afforestation In Cerrado Grasslands Using Dung Beetles Escarabeíneos Copro-Necrófagos Associados A Sistemas Pecuários Bett & Farias 2013 No Município De Lauro Müller Subtle Land-Use Change And Tropical Biodiversity: Dung Beetle Almeida et al. 2011 Communities In Cerrado Grasslands And Exotic Pastures Successional And Seasonal Changes In A Community Of Dung Beetles Neves et al. 2010 (Coleoptera: Scarabaeinae) In A Brazilian Tropical Dry Forest Using Dung Beetles To Evaluate The Effects Of Urbanization On Korasaki et al. 2012 Atlantic Forest Biodiversity

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Presencia Otoñal De Escarabajos Estercoleros Nativos Paracópridos Chang 2010 (Scarabaeidae:Scarabaeinae) En Renovales De Bosque Nativo Y Praderas Naturales Asociadas Land Sharing Vs. Land Sparing In The Dry Caribbean Lowlands A Dung Montoya-Molina et al. 2016 Beetles’ Perspective Actividad Diaria De Colonización Del Recurso Alimenticio En Un Noriega et al. 2008 Ensamblaje De Escarabajos Coprófagos (Coleoptera: Scarabaeidae) En La Amazonía Colombiana Altitudinal Variation Of Dung Beetle (Scarabaeidae: Scarabaeinae) Escobar et al. 2005 Assemblages In The Colombian Andes Comparación De La Composición Y Riqueza De Especies De Amézquita et al. 1999 Escarabajos Coprófagos En Remanentes De Bosque De La Orinoquia Colombiana Diversidad De Coleopteros Coprofagos (Scarabaeidae: Scarabaeinae) Escobar 2000 En Un Mosaico De Habitats En La Reserva Natural Nukak, Guaviare, Colombia Diversidad De Escarabajos Carabidae Y Scarabaeidae De Un Bosque Uribe et al. 2013 Tropical En El Magdalena Medio Colombiano Diversidad De Escarabajos Coprófagos (Coleoptera: Scarabaeidae) En Noriega et al. 2007 Un Bosque De Galería Con Tres Estadios De Alteración Diversity And Composition Of Dung Beetle (Scarabaeinae) Escobar 2004 Assemblages In A Heterogeneous Andean Landscape Diversity And Habitat Use Of Dung Beetles In A Restored Andean Medina et al. 2002 Landscape Dung Beetle (Scarabaeidae: Scarabaeinae) Atractted To Wooly Monkey Castellanos et al. 1999 (Lagothrix Lagothricha Humboldt) Dung At Tinigua National Park, Colombia Effects Of Clearing In A Tropical Rain Forest On The Composition Of Howden & Nealis 1975 The Coprophagous Scarab Beetle Fauna (Coleoptera)

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How A Locality Can Have So Many Species? Acase Study With Dung Noriega 2015 Beetles (Coleoptera: Scarabaeinae) In A Tropical Rain Forest In Colombia Patrones De Distribucion De Escarabajos Coprofagos Garcia et al. 1997 (Coleoptera:Scarabaeidae) En Relicto Del Bosque Altoandino, Cordillera Oriental De Colombia Rapid Turnover And Edge Effects In Dung Beetle Assemblages Spector & Ayzama 2003 (Scarabaeidae) At A Bolivian Neotropical Forest-Savanna Ecotone Superfamilia Scarabaeoidea (Insecta: Coleoptera) Como Elemento Otavo et al. 2013 Bioindicador De Perturbación Antropogénica -En Un Parque Nacional Amazónico The Potential Value Of Agroforestry To Dung Beetle Diversity In The Neita & Escobar 2012 Wet Tropical Forests Of The Pacific Lowlands Of Colombia Assessing Sustainability Indicators For Tropical Forests: Spatio- Aguilar-Amuchastegui & 2007 Temporal Heterogeneity, Logging Intensity, And Dung Beetle Henebry Communities Dung Beetle And Terrestrial Mammal Diversity In Forests, Indigenous Harvey et al. 2006 Agroforestry Systems And Plantain Monocultures In Talamanca, Costa Rica Dung-Beetles (Coleoptera: Scarabeidae) From The Zona Protectora Las González-Maya & Mata- 2008 Tablas, Talamanca, Costa Rica Lorenzen Seasonal Change In Abundance Of Large Nocturnal Dung Beetles Janzen 1983 (Scarabaeidae) In A Costa Rican Deciduous Forest And Adjacent Horse Pasture Temporal Shifts In Dung Beetle Community Structure Within A Escobar et al. 2008 Protected Area Of Tropical Wet Forest: A 35-Year Study And Its Implications For Long-Term Conservation The Importance Of Microhabitat For Biodiversity Sampling Mehrabi et al. 2014

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Dung Beetles (Coleoptera: Scarabaeinae) Diversity In An Altitudinal Celi et al. 2004 Gradient In The Cutucú Range, Morona Santiago, Ecuadorian Amazon Escarabájos Coprófagos (Coleoptera: Scarabaeidae: Scarabaeinae) Ximena 2013 Como Indicadores De Diversidad Biológica En La Estación Biológica Pindo Mirador. Pastaza-Ecuador Short Term Response Of Dung Beetle Communities To Disturbance By Carpio et al. 2009 Road Construction In The Ecuadorian Amazon Structure Of Dung Beetle Communities (Coleoptera: Scarabaeinae) In Domínguez et al. 2015 An Altitudinal Gradient Of Neotropical Dry Forest Dung Beetle Assemblages In Forests And Pastures Of El Salvador: A Horgan 2008 Functional Comparison Dung Beetles In Pasture Landscapes Of Central America: Proliferation Horgan 2007 Of Synanthropogenic Species And Decline Of Forest Specialists Shady Field Boundaries And The Colonisation Of Dung By Horgan 2002 Coprophagous Beetles In Central American Pastures Dung Beetle Community (Coleoptera: Scarabaeidae: Scarabaeinae) In Avedaño-Mendoza et al. 2005 A Tropical Landscape At The Lachua Region, Guatemala Dung Beetles As Indicators For Rapid Impact Assessments: Evaluating Bicknella et al. 2014 Best Practice Forestry In The Neotropics

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Comunidad De Escarabajos Coprófagos (Coleoptera: Scarabaeidae: Rivera & Cantarero 2011 Scarabaeinae) En Hábitats Bajo Distinta Intensidad De Uso En Yuscarán, Honduras Scarabaeinae Community Biology And Morphology In A Tropical Creedy 2010 Montane Cloud Forest Instability Of Copronecrophagous Beetle Assemblages (Coleoptera: Halffter et al. 2007 Scarabaeinae) In A Mountainous Tropical Landscape Of Mexico Response Of Dung Beetle Diversity To Human-Induced Changes In A Halffter et al. 2002 Tropical Landscape Historical And Ecological Determinants Of Dung Beetle Assemblages Halffter et al. 2012 In Two Arid Zones Of Central Mexico Grazing Promotes Dung Beetle Diversity In The Xeric Landscape Of A Verdú et al. 2007 Mexican Biosphere Reserve Effect Of Uncontrolled Forest Fires On The Coprophagous Beetle Arellano et al. 2014 Assemblages (Coleoptera: Scarabaeidae) In A Temperate Forest In Central Mexico Necrocolous Beetles (Scarabaeidae: Scarabaeinae, Silphidae And González-Hernández et al. 2015 Trogidae) From Bosquelos Colomos, Guadalajara, Jalisco, Mexico Removal Rates Of Native And Exotic Dung By Dung Beetles Amézquita et al. 2010 (Scarabaeidae: Scarabaeinae) In A Fragmented Tropical Rain Forest Effects Of Season And Vegetation Type On Community Organization Andressen 2005 Of Dung Beetles In A Tropical Dry Forest Dung Beetle (Coleoptera: Scarabaeidae: Scarabaeinae) Diversity In Navarrete et al. 2008 Continuous Forest, Forest Fragments And Cattle Pastures In A Landscape Of Chiapas, Mexico: The Effects Of Anthropogenic Changes Forest Loss And Matrix Composition Are The Major Drivers Shaping Sanchez-De-Jesus et al. 2016 Dung Beetle Assemblages In A Fragmented Rainforest Dung Beetles In Continuous Forest, Forest Fragments And In An Estrada et al. 2002 Agricultural Mosaic Habitat Island At Los Tuxtlas, Mexico

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Frog, Bat, And Dung Beetle Diversity In The Cloud Forest And Coffee Pineda et al. 2005 Agroecosystems Of Veracruz, Mexico Diversity Of Dung And Carrion Beetles In A Disturbed Mexican Tropical Arellano et al. 2005 Montane Cloud Forest And On Hade Coffee Plantations Dung Beetle Assemblages In Primary Forest And Disturbed Habitats In Andressen 2008 A Tropical Dry Forest Landscape In Western Mexico Dung Beetles Attracted To Mammalian Herbivore (Alouatta Palliata) Estrada et al. 1993 And Omnivere (Nasua Narica) Dung In The Tropical Rain Forest Of Los Tuxlas Mexico Carrion Removal Rates And Diel Activity Of Necrophagous Beetles Amézquita et al 2011 (Coleoptera: Scarabaeinae) In A Fragmented Tropical Rain Forest Impact Of The Activity Of Dung Beetles (Coleoptera: Carabaeidae: Anduaga 2004 Scarabaeinae) Inhabiting Pasture And In Durango, Mexico Response Of Dung Beetle Assemblages To Landscape Structure In Arellano et al. 2008 Remnant Natural And Modified Habitats In Southern Mexico Dung Beetle (Coleoptera: Scarabaeinae) Diversity And Seasonality In Basto-Estrella et al. 2014 Response To Use Of Macrocyclic Lactones At Cattle Ranches In The Mexican Neotropics Biogeographical And Ecological Factors Affecting The Altitudinal Lobo & Halffter 2000 Variation Of Mountainous Communities Of Coprophagous Beetles (Coleoptera: Scarabaeoidea): A Comparative Study How Dung Beetles Respond To A Humanmodified Variegated Ros et al. 2012 Landscape In Mexican Cloud Forest: A Study Of Biodiversity Integrating Ecological And Biogeographical Perspectives Dung And Carrion Beetles In Tropical Rain Forest Fragments And Estrada et al. 1998 Agricultural Habitats At Los Tuxtlas, Mexico The Impact Of Grazing On Dung Beetle Diversity Depends On Both Barragán et al. 2014 Biogeographical And Ecological Context

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Biogeographical Affinities And Species Richness Of Arriaga et al. 2012 Copronecrophagous Beetles (Scarabaeoidea) In The Southeastern Mexican High Plateau Short-Term Temporal Variability In The Abundance Of Tropical Dung Andressen 2008 Beetles Dung Beetles In Pasture Landscapes Of Central America: Proliferation Horgan 2007 Of Synanthropogenic Species And Decline Of Forest Specialists Abundancia Y Diversidad De Escarabajos Coprófagos Y Mariposas Hernández et al. 2003 Diurnas En Un Paisaje Ganadero En El Departamento De Rivas, Nicaragua Efecto De La Composición Y Estructura Del Paisaje Y Del Hábitat Mendoza 2009 Sobre Distintos Grupos Taxonómicos En Un Agropaisaje Em Matiguás, Nicaragua Scarabaeidae (Coleoptera) Copronecrofagos Interesantes Del Barbero 2001 Departamento De Rio San Juan, Nicaragua Possible Indirect Effects Of Mammal Hunting On Dung Beetle Andressen & Laurance 2007 Assemblages In Panama Aggregation And Coexistence Of Dung Beetles In Montane Rain Forest Horgan 2006 And Deforested Sites In Central Peru Invasion And Retreat: Shifting Assemblages Of Dung Beetles Amidst Horgan 2009 Changing Agricultural Landscapes In Central Peru Scarabaeinae Beetle (Coleoptera; Scarabaeidae) Species Collected In Gill 1997 The Cordillera Del Cóndor Seed Dispersal By Monkeys And The Fate Of Ispersed Seeds In A Andressen 1999 Peruvian Rain Forest' Structure Of The Scarab Beetle Fauna (Coleoptera: Scarabaeoidea) In Martínez et al. 2009 Forest Remnants Of Western Puerto Rico Coprophagous Beetles (Coleoptera: Scarabaeoidea) In Uruguayan Morelli et al. 2002 Prairies: Abundance, Diversity And Seasonal Occurrence

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Differences In Coprophilous Beetle Communities Structure In Sierra De González-Vainer et al. 2012 Minas (Uruguay): A Mosaic Landscape Relevamiento De Los Coleópteros Coprofilos Y Necrófilos De Sierra De González-Vainer & Morelli 2008 Minas, Uruguay (Insecta: Coleoptera) Seleccion De Macrohabitat Y Variacion Estacional De Coleopteros Pons 2010 Coprofagos En Sierra De Minas, Uruguay Extinction Order And Altered Community Structure Rapidly Disrupt Larsen et al. 2005 Ecosystem Functioning 3 4 5 Tabela 1-2. Neotropical Dung Beetle Local Richness Database ID Latitude Longitude S N Trap Habitat Bait Type Ecorregion hour type 1 -26.275 -59.967 20 445 1440 Closed Both Humid Chaco 2 -27.030 -59.638 17 324 1440 Closed Both Humid Chaco 3 -27.318 -58.950 21 3238 31104 Closed Both Humid Chaco 4 -25.750 -54.560 27 9682 11520 Both Onivore dung Alto Paraná Atlantic forests 5 -27.028 -59.640 29 3356 105840 Closed Both Humid Chaco 6 17.264 -88.786 15 169 2520 Closed Both Petén-Veracruz moist forests 7 -16.233 -66.417 30 2810 4800 Both Onivore dung Bolivian Yungas 9 -14.579 -60.908 73 4050 1296 Closed Onivore dung Chiquitano dry forests 12 -2.517 -55.000 17 5874 4320 Closed Both Madeira-Tapajós moist forests 14 -2.500 -60.000 61 14657 6912 Closed Both Uatuma-Trombetas moist forests 16 -0.883 -52.600 89 30565 40320 Closed Both Marajó várzea 17 -8.500 -35.833 30 5893 261744 Closed Both Pernambuco interior forests 18 -13.951 -41.090 21 2143 2880 Both Both Caatinga 19 -12.883 -39.850 16 1581 36864 Open Both Caatinga 20 -15.917 -47.850 102 6879 125856 Both Both Cerrado

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24 -21.591 -44.626 17 400 1152 Closed Both Cerrado 25 -21.588 -44.612 13 456 1152 Closed Both Cerrado 26 -21.583 -44.631 11 307 1152 Closed Both Cerrado 27 -21.570 -44.633 18 84 1152 Closed Both Campos Rupestres montane savanna 28 -21.590 -44.626 11 131 1152 Open Both Cerrado 29 -21.586 -44.627 12 156 1152 Open Both Cerrado 30 -21.588 -44.616 13 65 1152 Open Both Cerrado 31 -21.587 -44.623 9 34 1152 Open Both Cerrado 32 -21.581 -44.634 10 49 1152 Open Herbivore Cerrado dung 33 -21.592 -44.536 8 170 1152 Open Herbivore Campos Rupestres montane savanna dung 34 -21.590 -44.566 10 85 1152 Open Herbivore Campos Rupestres montane savanna dung 35 -21.588 -44.617 12 48 1152 Open Onivore dung Cerrado 36 -21.593 -44.584 7 39 1152 Open Herbivore Campos Rupestres montane savanna dung 37 -21.594 -44.586 13 141 1152 Open Onivore dung Campos Rupestres montane savanna 38 -21.591 -44.576 11 160 1152 Open Both Campos Rupestres montane savanna 39 -21.589 -44.566 7 38 1152 Open Rotten meat Campos Rupestres montane savanna 41 -21.990 -55.323 33 9229 12096 Open Both Cerrado 42 -22.145 -55.026 36 3181 12096 Open Onivore dung Cerrado 43 -22.990 -54.916 35 4731 12096 Open Both Alto Paraná Atlantic forests 46 -20.763 -55.730 31 13809 7200 Both Both Cerrado 47 -20.763 -55.730 26 1027 7200 Both Both Cerrado 49 -6.581 -35.023 14 3634 11520 Open Both Pernambuco interior forests 50 -6.733 -35.133 29 3533 10368 Closed Both Pernambuco interior forests 51 -23.449 -51.246 35 4016 20160 Both Both Alto Paraná Atlantic forests 52 -8.701 -35.099 25 1724 2880 Open Both Pernambuco coastal forests

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53 -8.564 -35.168 11 367 2400 Closed Both Pernambuco coastal forests 54 -8.617 -37.150 13 1097 19008 Open Both Caatinga 55 -7.810 -34.940 40 2560 27648 Both Both Pernambuco interior forests 57 -7.833 -35.100 35 7267 1152 Both Onivore dung Pernambuco coastal forests 61 -31.353 -54.014 30 4573 75240 Open Onivore dung Uruguayan savanna 62 -29.676 -53.721 33 19699 320760 Closed Onivore dung Alto Paraná Atlantic forests 63 -29.642 -53.586 28 1611 51840 Closed Onivore dung Alto Paraná Atlantic forests 64 -29.686 -50.804 29 1289 25344 Closed Onivore dung Alto Paraná Atlantic forests 65 -27.417 -48.581 11 487 2400 Closed Both Serra do Mar coastal forests 66 -27.087 -48.619 13 380 2400 Closed Onivore dung Serra do Mar coastal forests 67 -27.725 -48.538 14 659 2400 Closed Herbivore Serra do Mar coastal forests dung 68 -27.531 -48.513 16 1478 2400 Closed Onivore dung Southern Atlantic mangroves 69 -27.383 -51.200 33 1502 9600 Both Onivore dung Araucaria moist forests 70 -27.733 -48.800 17 426 1920 Closed Both Serra do Mar coastal forests 71 -27.383 -51.200 44 3454 38400 Both Onivore dung Araucaria moist forests 72 -20.367 -51.400 26 1976 6048 Closed Onivore dung Alto Paraná Atlantic forests 75 -23.417 -45.233 27 602 960 Closed Both Serra do Mar coastal forests 76 -23.300 -45.083 24 443 960 Closed Onivore dung Serra do Mar coastal forests 77 -23.567 -45.417 19 657 960 Closed Rotten fruit Serra do Mar coastal forests 78 -23.317 -44.833 25 2037 960 Closed Rotten fruit Serra do Mar coastal forests 79 -24.033 -47.967 23 301 960 Closed Rotten fruit Alto Paraná Atlantic forests 80 -24.183 -47.917 15 207 960 Closed Both Serra do Mar coastal forests 81 -24.300 -48.400 18 182 960 Closed Onivore dung Alto Paraná Atlantic forests 82 -15.350 -56.792 98 17635 38400 Both Onivore dung Cerrado 83 -10.163 -41.353 14 1073 74880 Open Onivore dung Caatinga 84 -8.500 -35.833 45 4218 115200 Both Onivore dung Pernambuco interior forests 85 -20.471 -55.787 16 2290 1920 Open Onivore dung Cerrado 86 -16.344 -56.331 14 1680 10800 Both Both Pantanal

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87 -21.473 -44.651 40 3093 13056 Both Onivore dung Campos Rupestres montane savanna 88 -28.357 -49.452 17 348 7680 Open Both Serra do Mar coastal forests 89 -21.473 -44.651 66 4996 10080 Open Onivore dung Campos Rupestres montane savanna 90 -14.869 -44.001 38 2752 46080 Both Onivore dung Caatinga 91 -23.450 -51.250 26 835 18480 Closed Both Alto Paraná Atlantic forests 92 -23.317 -51.183 22 702 18480 Closed Onivore dung Alto Paraná Atlantic forests 93 -23.500 -51.000 14 182 18480 Closed Onivore dung Alto Paraná Atlantic forests 95 10.516 -73.088 32 5349 5184 Both Onivore dung Guajira Barranquilla xeric scrub 116 -3.808 -70.267 15 80 10368 Closed Rotten meat Iquitos várzea 117 -3.717 -70.272 17 91 10368 Closed Both Solimões-Japurá moist forests 118 -3.675 -70.276 17 61 10368 Closed Both Solimões Japurá moist forests 119 5.525 -76.560 19 13414 31680 Closed Both Chocó Darién moist forests 120 10.422 -84.015 19 1328 25200 Closed Both Isthmian Atlantic moist forests 121 9.624 -82.864 43 20003 52800 Open Onivore dung Isthmian Atlantic moist forests 122 9.531 -83.111 48 64040 52800 Closed Both Isthmian Atlantic moist forests 123 9.579 -83.007 39 19458 237600 Closed Both Isthmian Atlantic moist forests 124 9.556 -82.966 30 28959 46200 Closed Both Isthmian Atlantic moist forests 127 10.430 -84.007 38 4679 2604 Closed Herbivore Isthmian Atlantic moist forests dung 128 8.481 -83.589 31 17744 11520 Closed Both Isthmian Pacific moist forests 129 -2.810 -77.989 105 5655 19200 Closed Herbivore Eastern Cordillera real montane forests dung 130 -1.458 -78.081 16 282 5760 Closed Onivore dung Eastern Cordillera real montane forests 131 -1.455 -78.083 15 144 5760 Closed Onivore dung Eastern Cordillera real montane forests 132 -0.644 -75.913 69 4895 3456 Closed Onivore dung Napo moist forests 133 -4.040 -79.349 6 7422 60480 Closed Onivore dung Eastern Cordillera real montane forests 138 15.909 -90.617 33 7158 5184 Closed Herbivore Petén-Veracruz moist forests dung

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139 4.237 -58.947 34 1290 11520 Closed Herbivore Guianan moist forests dung 140 4.083 -52.667 43 1851 76800 Closed Herbivore Guianan moist forests dung 141 4.550 -52.200 33 2397 20736 Closed Herbivore Guianan moist forests dung 142 4.083 -52.667 63 2525 3840 Closed Herbivore Guianan moist forests dung 143 4.850 -53.067 50 4408 6000 Closed Rotten meat Guianan moist forests 147 13.894 -86.856 9 214 1440 Closed Rotten meat Central American pine-oak forests 148 13.872 -86.855 10 143 1440 Closed Herbivore Central American pine-oak forests dung 149 13.895 -86.852 7 88 1440 Open Onivore dung Central American pine-oak forests 150 13.893 -86.858 9 178 1440 Closed Onivore dung Central American pine-oak forests 151 13.892 -86.856 6 95 1440 Closed Onivore dung Central American pine-oak forests 152 13.890 -86.853 4 77 1440 Open Onivore dung Central American pine-oak forests 153 13.895 -86.776 11 411 1440 Closed Onivore dung Central American dry forests 154 13.896 -86.777 9 320 1440 Closed Onivore dung Central American dry forests 155 13.895 -86.781 8 88 1440 Open Onivore dung Central American dry forests 156 13.888 -86.771 10 404 1440 Closed Onivore dung Central American dry forests 157 13.889 -86.774 13 352 1440 Closed Onivore dung Central American dry forests 158 13.891 -86.775 12 192 1440 Open Onivore dung Central American dry forests 159 15.542 -88.264 25 681 10368 Closed Onivore dung Central American Atlantic moist forests 163 20.233 -98.900 14 57671 13680 Both Onivore dung Central Mexican matorral 164 18.325 -97.475 12 1172 10176 Both Onivore dung Tehuacán Valley matorral 165 20.233 -98.900 14 74672 27648 Both Onivore dung Central Mexican matorral 166 19.334 -98.366 4 180 11568 Closed Onivore dung Trans-Mexican Volcanic Belt pine-oak forests 167 20.708 -103.395 3 7 20832 Closed Onivore dung Bajío dry forests 192 19.469 -105.045 14 852 5760 Closed Both Jalisco dry forests

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193 19.469 -105.045 13 440 5760 Closed Both Jalisco dry forests 194 19.469 -105.045 3 105 5760 Closed Both Jalisco dry forests 195 16.836 -91.493 49 20539 182400 Both Both Chiapas montane forests 196 16.836 -91.493 43 9418 28224 Closed Onivore dung Chiapas montane forests 197 18.619 -95.084 33 7332 57600 Closed Both Sierra de los Tuxtlas 198 19.369 -96.376 17 1251 36288 Closed Both Veracruz dry forests 200 19.469 -105.045 16 1295 5760 Both Both Jalisco dry forests 208 24.199 -104.299 7 140 2160 Open Herbivore Meseta Central matorral dung 209 24.199 -104.299 6 770 2160 Open Herbivore Meseta Central matorral dung 210 24.199 -104.299 7 2520 2160 Open Herbivore Meseta Central matorral dung 211 16.862 -93.179 28 4109 51840 Closed Both Petén-Veracruz moist forests 212 21.600 -88.150 17 53362 13824 Open Herbivore Yucatán dry forests dung 214 19.995 -97.502 10 491 3456 Both Onivore dung Trans-Mexican Volcanic Belt pine-oak forests 215 19.923 -97.379 16 1006 3456 Both Onivore dung Oaxacan montane forests 216 19.933 -97.348 18 2261 3456 Both Onivore dung Oaxacan montane forests 217 19.996 -97.527 15 489 3456 Both Onivore dung Trans-Mexican Volcanic Belt pine-oak forests 219 21.242 -98.535 21 2375 3600 Closed Herbivore Veracruz moist forests dung 220 21.170 -98.585 14 385 3600 Open Herbivore Veracruz moist forests dung 221 21.007 -98.865 22 1768 3600 Closed Herbivore Veracruz montane forests dung 222 20.963 -98.775 20 1920 3600 Open Herbivore Veracruz montane forests dung 223 20.168 -98.556 6 97 3600 Closed Herbivore Sierra Madre Oriental pine-oak forests dung

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224 20.120 -98.465 8 1471 3600 Open Herbivore Sierra Madre Oriental pine-oak forests dung 225 19.971 -98.938 9 164 3600 Open Herbivore Central Mexican matorral dung 226 20.141 -98.804 10 527 3600 Open Herbivore Central Mexican matorral dung 227 19.338 -97.475 1 7 82944 Both Rotten meat Tehuacán Valley matorral 228 19.464 -97.387 3 78 82944 Both Rotten meat Tehuacán Valley matorral 229 19.506 -97.521 3 477 82944 Both Rotten meat Tehuacán Valley matorral 230 19.293 -97.355 5 85 82944 Open Rotten meat Tehuacán Valley matorral 231 19.474 -97.441 3 49 82944 Open Rotten meat Tehuacán Valley matorral 232 19.498 -105.044 13 1170 2880 Closed Herbivore Jalisco dry forests dung 234 11.500 -85.883 31 15616 73824 Both Onivore dung Central American dry forests 235 12.833 -85.450 33 3693 73824 Both Onivore dung Central American Atlantic moist forests 239 -11.126 -75.363 45 5006 43200 Closed Onivore dung Peruvian Yungas 240 -11.080 -75.382 14 152 960 Closed Onivore dung Southwest Amazon moist forests 241 -11.078 -75.382 17 181 1440 Closed Onivore dung Southwest Amazon moist forests 242 -11.080 -75.382 16 259 960 Open Onivore dung Southwest Amazon moist forests 243 -11.080 -75.382 11 115 1440 Closed Onivore dung Southwest Amazon moist forests 244 -11.070 -75.380 9 118 960 Open Onivore dung Southwest Amazon moist forests 245 -11.065 -75.367 12 106 1440 Closed Onivore dung Southwest Amazon moist forests 246 -11.063 -75.382 8 45 1440 Open Onivore dung Southwest Amazon moist forests 247 -11.078 -75.407 17 314 960 Closed Onivore dung Peruvian Yungas 248 -11.078 -75.407 10 33 960 Closed Onivore dung Peruvian Yungas 249 -11.090 -75.403 15 87 960 Closed Onivore dung Peruvian Yungas 250 -11.090 -75.403 2 2 960 Open Onivore dung Peruvian Yungas 253 -11.137 -75.335 12 67 960 Closed Onivore dung Peruvian Yungas 255 -11.127 -75.357 5 31 960 Open Onivore dung Peruvian Yungas

210

257 -11.175 -75.313 11 40 960 Closed Onivoredung Peruvian Yungas 258 -11.175 -75.313 10 87 960 Open Onivore dung Peruvian Yungas 259 -11.168 -75.303 15 66 960 Closed Onivore dung Peruvian Yungas 265 -34.536 -55.356 6 1341 36288 Both Herbivore Uruguayan savanna dung 266 -34.516 -55.331 10 1746 8064 Both Both Uruguayan savanna 267 -34.517 -55.333 3 1263 4032 Both Herbivore Uruguayan savanna dung 6

211

1 2 Figure S1.1. Log of Dung Beetle Richness and Abundance relation with Trap Hour.

212

3 4 Figure S1.2. Scarabaeinae Richness significative path relations.

5 6 Figure S1.3. Scarabaeinae Abundance significative paths relations

213

7 8 Figure S1.4 Mammal Richness significative paths relations.

214

1 Capítulo 2 Supplementary Information S2

2 Tabela 2-1. Neotropical Dung Beetle Local Richness database ID Latitude Longitude Habitat Type Bait type N S obs Completeness S est 1 -28.55 -49.6 Closed Rotten Meat 24 7 0.9233 7.958 2 -28.55 -49.6 Closed Omnivore Dung 140 11 0.9858 12.986 3 -28.55 -49.59 Closed Rotten Meat 52 8 0.9438 10.207 4 -28.55 -49.59 Closed Omnivore Dung 217 12 0.9908 13.991 5 -27.99 -49.38 Closed Rotten Meat 46 8 0.9575 9.957 6 -27.99 -49.38 Closed Omnivore Dung 533 8 1 8 7 -27.89 -48.86 Closed Rotten Meat 11 6 0.8601 6.606 8 -27.89 -48.86 Closed Omnivore Dung 288 9 0.9931 10.993 9 -27.75 -48.82 Closed Herbivore Dung 65 11 0.9546 13.954 10 -27.75 -48.82 Closed Omnivore Dung 111 12 0.9554 16.129 11 -27.75 -48.82 Closed Omnivore Dung 94 11 0.9794 11.66 12 -27.75 -48.82 Closed Carnivore Dung 56 11 0.9477 13.21 13 -27.74 -48.82 Closed Herbivore Dung 1 1 1 1 14 -27.74 -48.82 Closed Omnivore Dung 12 2 1 2 15 -27.74 -48.82 Closed Omnivore Dung 12 3 0.859 3.917 16 -27.74 -48.82 Closed Carnivore Dung 2 2 0.6667 2.5 17 -27.74 -48.81 Closed Rotten Meat 297 15 0.9933 15.664 18 -27.74 -48.81 Closed Herbivore Dung 3 1 1 1 19 -27.74 -48.81 Closed Omnivore Dung 33 11 0.8219 19.727 20 -27.74 -48.81 Closed Omnivore Dung 3 3 0.3333 5 21 -27.74 -48.81 Closed Carnivore Dung 8 3 0.9028 3.438 22 -27.74 -48.81 Closed Omnivore Dung 483 16 0.9959 17.996 23 -27.74 -48.8 Closed Rotten Meat 256 14 0.9844 17.984

215

24 -27.74 -48.8 Closed Herbivore Dung 5 2 1 2 25 -27.74 -48.8 Closed Omnivore Dung 3 2 0.8333 2.333 26 -27.74 -48.8 Closed Omnivore Dung 15 8 0.832 9.05 27 -27.74 -48.8 Closed Carnivore Dung 3 3 0.3333 5 28 -27.74 -48.8 Closed Omnivore Dung 219 13 0.9864 15.986 29 -27.63 -48.88 Closed Rotten Meat 193 12 0.9845 14.984 30 -27.63 -48.88 Closed Omnivore Dung 237 18 0.9705 42.397 31 -27.51 -51.46 Closed Dung and Carrion 201 19 0.9704 21.985 32 -27.5 -51.47 Closed Dung and Carrion 647 31 0.9877 38.988 33 -27.5 -51.44 Closed Dung and Carrion 299 12 0.9833 24.458 34 -27.49 -51.47 Closed Dung and Carrion 199 22 0.9548 62.296 35 -27.49 -51.44 Closed Dung and Carrion 107 13 0.9908 13.495 36 -27.49 -51.2 Closed Dung and Carrion 60 10 0.9339 17.867 37 -27.48 -51.22 Closed Dung and Carrion 323 14 0.9784 22.141 38 -27.47 -51.26 Closed Dung and Carrion 12 6 0.6812 13.333 39 -27.47 -51.25 Closed Dung and Carrion 5 5 0.1111 13 40 -27.47 -51.24 Closed Dung and Carrion 173 13 0.9944 13.249 41 -27.46 -51.25 Closed Dung and Carrion 31 11 0.8751 14.871 42 -27.44 -51.51 Closed Dung and Carrion 205 21 0.966 29.127 43 -27.43 -51.05 Closed Dung and Carrion 50 13 0.9412 15.94 44 -27.43 -51.03 Closed Dung and Carrion 134 12 0.9628 21.925 45 -27.43 -48.86 Closed Rotten Meat 16 5 0.8897 5.938 46 -27.43 -48.86 Closed Omnivore Dung 196 15 0.9898 16.99 47 -27.43 -48.85 Closed Rotten Meat 45 12 0.8909 18.111 48 -27.43 -48.85 Closed Omnivore Dung 82 13 0.9765 13.659 49 -27.42 -51.22 Closed Dung and Carrion 13 7 0.5533 20.846 50 -27.38 -51.35 Closed Dung and Carrion 94 17 0.9583 18.979 51 -27.38 -51.22 Closed Dung and Carrion 54 16 0.8909 21.889

216

52 -27.37 -51.2 Closed Dung and Carrion 68 9 0.9416 16.882 53 -27.36 -51.34 Closed Dung and Carrion 90 14 0.9225 38.228 54 -27.36 -51.19 Closed Dung and Carrion 29 10 0.8306 19.655 55 -27.35 -51.37 Closed Dung and Carrion 103 13 0.9419 30.825 56 -27.35 -51.36 Closed Dung and Carrion 314 19 0.9905 20.495 57 -27.34 -51.34 Closed Dung and Carrion 43 10 0.8859 16.105 58 -27.3 -51.24 Closed Dung and Carrion 156 18 0.9811 19.118 59 -27.28 -51.26 Closed Dung and Carrion 44 12 0.8647 29.591 60 -27.1 -48.9 Closed Rotten Meat 85 9 1 9 61 -27.1 -48.9 Closed Omnivore Dung 168 8 0.9942 8.249 62 -27.1 -48.89 Closed Rotten Meat 69 11 0.9281 20.855 63 -27.1 -48.89 Closed Omnivore Dung 98 11 0.9902 11.247 64 -27.1 -48.88 Closed Rotten Meat 156 10 1 10 65 -27.1 -48.88 Closed Omnivore Dung 209 13 0.9858 14.493 66 -26.92 -48.86 Closed Herbivore Dung 5 1 1 1 67 -26.92 -48.86 Closed Omnivore Dung 53 4 0.9445 6.943 68 -26.92 -48.86 Closed Rotten Meat 43 6 0.9778 6.488 69 -26.92 -48.86 Open Omnivore Dung 2 1 1 1 70 -26.92 -48.86 Open Rotten Meat 12 2 1 2 71 -26.92 -48.83 Closed Omnivore Dung 42 4 0.9773 4.488 72 -26.92 -48.83 Closed Rotten Meat 20 6 1 6 73 -26.92 -48.83 Open Omnivore Dung 5 2 1 2 74 -26.92 -48.83 Open Rotten Meat 5 1 1 1 75 -26.9 -49.12 Closed Omnivore Dung 397 12 0.9975 12.499 76 -26.88 -49.2 Closed Herbivore Dung 7 1 1 1 77 -26.88 -49.2 Closed Omnivore Dung 48 5 0.9178 10.875 78 -26.88 -49.2 Closed Rotten Meat 27 2 1 2 79 -26.85 -48.99 Closed Omnivore Dung 12 4 0.8472 5.833

217

80 -26.85 -48.99 Closed Rotten Meat 14 5 0.8762 5.929 81 -26.82 -49.04 Closed Omnivore Dung 39 7 0.95 8.949 82 -26.82 -49.04 Closed Rotten Meat 18 4 1 4 83 -26.82 -49.04 Open Omnivore Dung 1 1 1 1 84 -26.82 -49.04 Open Rotten Meat 9 3 1 3 85 -26.82 -49.02 Closed Omnivore Dung 27 6 0.9679 6.241 86 -26.82 -49.02 Closed Rotten Meat 47 8 0.9584 9.957 87 -26.82 -49.02 Open Omnivore Dung 5 3 0.7333 3.8 88 -26.82 -49.02 Open Rotten Meat 10 4 0.9308 4.225 89 -26.82 -48.99 Closed Omnivore Dung 46 13 0.8941 17.076 90 -26.82 -48.99 Closed Rotten Meat 41 10 1 10 91 -23.26 -46.36 Open Omnivore Dung 5 3 0.9 3.2 92 -23.26 -46.35 Closed Omnivore Dung 1106 45 0.9946 50.995 93 -23.26 -46.35 Open Omnivore Dung 96 14 0.9481 26.37 94 -23.25 -46.36 Closed Omnivore Dung 8 6 0.4167 14.75 95 -23.25 -46.35 Closed Omnivore Dung 13 7 0.8022 8.385 96 -23.25 -46.34 Closed Omnivore Dung 7 2 1 2 97 -23.25 -46.34 Open Omnivore Dung 17 3 1 3 98 -23.25 -46.33 Closed Omnivore Dung 32 11 0.877 18.75 99 -23.24 -46.36 Closed Omnivore Dung 6 5 0.3939 11.667 100 -23.24 -46.34 Open Omnivore Dung 4 2 1 2 101 -23.24 -46.33 Closed Omnivore Dung 20 9 0.9208 9.38 102 -23.23 -46.35 Closed Omnivore Dung 13 6 0.7085 11.538 103 -23.23 -46.34 Open Omnivore Dung 1 1 1 1 104 -23.15 -46.44 Closed Omnivore Dung 25 9 0.7665 17.64 105 -23.15 -46.44 Open Omnivore Dung 9 4 0.8025 5.778 106 -23.15 -46.43 Closed Omnivore Dung 117 18 0.949 26.923 107 -23.13 -46.43 Closed Omnivore Dung 7 7 0.0526 25

218

108 -23.13 -46.42 Closed Omnivore Dung 6 2 1 2 109 -23.13 -46.42 Open Omnivore Dung 1 1 1 1 110 -23.13 -46.41 Closed Omnivore Dung 37 13 0.8152 20.946 111 -23.13 -46.41 Open Omnivore Dung 9 4 0.8222 4.889 112 -23.12 -46.43 Closed Omnivore Dung 22 4 1 4 113 -23.12 -46.43 Open Omnivore Dung 5 2 1 2 114 -23.12 -46.42 Closed Omnivore Dung 12 9 0.5417 14.5 115 -23.12 -46.42 Open Omnivore Dung 4 3 0.625 4.5 116 -23.08 -46.35 Closed Omnivore Dung 141 16 0.9792 16.894 117 -23.08 -46.33 Closed Omnivore Dung 67 19 0.9416 21.627 118 -23.08 -46.24 Open Omnivore Dung 3 1 1 1 119 -23.08 -46.23 Closed Omnivore Dung 7 5 0.6494 6.929 120 -23.08 -46.22 Closed Omnivore Dung 5 4 0.4857 7.6 121 -23.08 -46.22 Open Omnivore Dung 6 3 0.881 3.417 122 -23.08 -46.21 Closed Omnivore Dung 18 5 0.8426 7.833 123 -23.08 -46.17 Closed Omnivore Dung 9 6 0.7333 7.333 124 -23.08 -46.17 Open Omnivore Dung 3 3 0.3333 5 125 -23.07 -46.35 Closed Omnivore Dung 35 10 0.9159 14.371 126 -23.07 -46.33 Closed Omnivore Dung 84 14 0.941 20.176 127 -23.07 -46.33 Open Omnivore Dung 4 2 1 2 128 -23.07 -46.24 Closed Omnivore Dung 8 7 0.2841 22.75 129 -23.07 -46.24 Open Omnivore Dung 1 1 1 1 130 -23.07 -46.14 Closed Omnivore Dung 12 5 0.859 5.917 131 -23.07 -46.14 Open Omnivore Dung 3 3 0.3333 5 132 -23.06 -46.33 Open Omnivore Dung 1 1 1 1 133 -23.06 -46.24 Closed Omnivore Dung 10 8 0.4414 16.1 134 -23.06 -46.24 Open Omnivore Dung 5 2 1 2 135 -23.06 -46.23 Open Omnivore Dung 10 2 1 2

219

136 -23.06 -46.22 Closed Omnivore Dung 27 5 0.9287 6.926 137 -23.06 -46.21 Open Omnivore Dung 10 3 1 3 138 -23.06 -46.17 Closed Omnivore Dung 3 2 0.8333 2.333 139 -23.06 -46.17 Open Omnivore Dung 1 1 1 1 140 -23.06 -46.16 Closed Omnivore Dung 144 11 0.9793 15.469 141 -23.06 -46.16 Open Omnivore Dung 2 2 0.6667 2.5 142 -23.06 -46.14 Closed Omnivore Dung 11 5 0.8485 5.909 143 -23.06 -46.14 Open Omnivore Dung 1 1 1 1 144 -23.05 -46.51 Closed Omnivore Dung 259 20 0.977 24.483 145 -23.05 -46.51 Open Omnivore Dung 7 4 0.7551 5.714 146 -23.05 -46.33 Open Omnivore Dung 3 2 0.8333 2.333 147 -23.05 -46.32 Closed Omnivore Dung 65 10 0.9855 10.246 148 -23.05 -46.21 Closed Omnivore Dung 7 3 0.7857 3.857 149 -23.05 -46.17 Closed Omnivore Dung 20 3 1 3 150 -23.05 -46.17 Open Omnivore Dung 1 1 1 1 151 -23.04 -46.5 Open Omnivore Dung 1 1 1 1 152 -23.04 -46.49 Closed Omnivore Dung 79 12 0.9756 12.658 153 -23.04 -46.33 Closed Omnivore Dung 215 12 0.9954 12.498 154 -23.03 -46.5 Closed Omnivore Dung 15 7 0.8091 11.2 155 -23.03 -46.5 Open Omnivore Dung 1 1 1 1 156 -23.03 -46.49 Closed Omnivore Dung 31 11 0.8127 16.806 157 -23.03 -46.49 Open Omnivore Dung 2 2 0.6667 2.5 158 -23.02 -46.49 Closed Omnivore Dung 18 9 0.7347 14.903 159 -23.01 -46.03 Closed Omnivore Dung 19 6 0.9053 6.947 160 -23 -46.05 Closed Omnivore Dung 11 3 1 3 161 -23 -46.05 Open Omnivore Dung 5 3 0.9 3.2 162 -23 -46.04 Closed Omnivore Dung 14 5 0.8762 5.929 163 -23 -46.04 Open Omnivore Dung 1 1 1 1

220

164 -23 -46.03 Closed Omnivore Dung 22 8 0.8755 9.432 165 -23 -46.03 Open Omnivore Dung 4 3 0.625 4.5 166 -22.99 -46.05 Closed Omnivore Dung 7 5 0.6494 6.929 167 -22.99 -46.03 Closed Omnivore Dung 27 8 0.8992 9.083 168 -22.99 -46.03 Open Omnivore Dung 3 3 0.3333 5 169 -22.99 -46.02 Closed Omnivore Dung 110 13 0.9639 18.945 170 -22.99 -46.02 Open Omnivore Dung 4 3 0.625 4.5 171 -22.98 -46.05 Closed Omnivore Dung 7 7 0.0526 25 172 -22.97 -46.48 Closed Omnivore Dung 8 3 0.8056 3.875 173 -22.97 -46.47 Closed Omnivore Dung 66 16 0.9105 21.909 174 -22.97 -46.3 Closed Omnivore Dung 43 12 0.888 15.052 175 -22.97 -46.3 Open Omnivore Dung 2 2 0.6667 2.5 176 -22.96 -46.48 Closed Omnivore Dung 61 15 0.9524 16.475 177 -22.96 -46.47 Open Omnivore Dung 2 2 0.6667 2.5 178 -22.96 -46.3 Closed Omnivore Dung 81 10 0.9515 12.634 179 -22.96 -46.3 Open Omnivore Dung 1 1 1 1 180 -22.96 -46.29 Closed Omnivore Dung 97 20 0.9188 25.278 181 -22.96 -46.29 Open Omnivore Dung 23 7 0.8312 12.739 182 -22.96 -46.28 Closed Omnivore Dung 74 10 0.9464 15.919 183 -22.96 -46.28 Open Omnivore Dung 11 5 0.8347 6.818 184 -22.96 -46.27 Open Omnivore Dung 2 2 0.6667 2.5 185 -22.95 -46.48 Closed Omnivore Dung 35 11 0.9233 11.729 186 -22.95 -46.47 Closed Omnivore Dung 28 6 0.9335 6.964 187 -22.95 -46.46 Closed Omnivore Dung 17 9 0.7131 20.765 188 -22.95 -46.46 Open Omnivore Dung 2 2 0.6667 2.5 189 -22.95 -46.45 Closed Omnivore Dung 28 10 0.8622 13.857 190 -22.95 -46.3 Closed Omnivore Dung 30 5 1 5 191 -22.95 -46.3 Open Omnivore Dung 1 1 1 1

221

192 -22.94 -46.46 Closed Omnivore Dung 98 19 0.96 20.98 193 -22.94 -46.44 Closed Omnivore Dung 85 13 0.9658 14.112 194 -22.94 -46.44 Open Omnivore Dung 18 6 0.8396 10.25 195 -22.94 -46.3 Closed Omnivore Dung 9 3 0.8222 3.889 196 -22.94 -46.29 Open Omnivore Dung 2 2 0.6667 2.5 197 -22.94 -46.21 Closed Omnivore Dung 20 10 0.656 29.95 198 -22.85 -46.43 Open Omnivore Dung 4 4 0.1818 8.5 199 -22.85 -46.42 Closed Omnivore Dung 81 14 0.9263 28.815 200 -22.85 -46.42 Open Omnivore Dung 17 5 0.8306 9.235 201 -22.85 -46.41 Closed Omnivore Dung 100 11 0.9804 11.99 202 -22.84 -46.42 Closed Omnivore Dung 43 10 0.9091 13.907 203 -22.84 -46.42 Open Omnivore Dung 38 6 0.8966 11.842 204 -22.83 -46.41 Open Omnivore Dung 10 2 1 2 205 -22.83 -46.4 Closed Omnivore Dung 15 5 0.8133 7.8 206 -20.46 -49.67 Closed Rotten Meat 104 9 0.9617 16.923 207 -20.46 -49.67 Closed Omnivore Dung 584 10 0.9966 10.998 208 -20.45 -55.62 Open Herbivore Dung 1351 14 0.9985 14.666 209 -20.44 -55.7 Open Herbivore Dung 11 4 0.8485 4.909 210 -20.44 -55.62 Open Herbivore Dung 435 8 0.9977 8.499 211 -20.44 -49.62 Open Rotten Meat 11 4 0.9242 4.455 212 -20.44 -49.62 Open Omnivore Dung 62 9 0.9526 11.214 213 -20.43 -55.7 Open Herbivore Dung 505 7 1 7 214 -19.58 -57.03 Closed Omnivore Dung 336 14 0.9941 14.997 215 -19.57 -57.03 Open Omnivore Dung 52 8 0.9815 8.49 216 -16.7 -48.84 Open Herbivore Dung 1 1 1 1 217 -16.7 -48.84 Open Omnivore Dung 9 3 0.8222 3.889 218 -16.7 -48.81 Closed Herbivore Dung 18 5 0.8426 7.833 219 -16.7 -48.81 Closed Omnivore Dung 16 5 0.8828 6.875

222

220 -16.7 -48.81 Open Herbivore Dung 5 3 0.7333 3.8 221 -16.7 -48.81 Open Omnivore Dung 86 8 1 8 222 -16.7 -48.81 Open Rotten Meat 3 2 0.8333 2.333 223 -16.69 -48.84 Closed Herbivore Dung 4 1 1 1 224 -16.69 -48.84 Closed Omnivore Dung 94 11 0.9792 11.989 225 -16.69 -48.84 Closed Rotten Meat 5 4 0.4857 7.6 226 -16.63 -48.67 Closed Herbivore Dung 17 4 0.8339 6.824 227 -16.63 -48.67 Closed Omnivore Dung 76 12 0.9221 17.921 228 -16.63 -48.67 Closed Rotten Meat 20 3 1 3 229 -16.63 -48.67 Open Herbivore Dung 1 1 1 1 230 -16.63 -48.67 Open Omnivore Dung 2 1 1 1 231 -16.602 -49.2712 Open Herbivore Dung 794 10 1 10 232 -16.6 -49.27 Closed Herbivore Dung 23 4 0.8752 6.87 233 -16.6 -49.27 Closed Omnivore Dung 209 6 0.9953 6.498 234 -16.6 -49.27 Closed Rotten Meat 10 4 0.8364 4.9 235 -16.6 -49.27 Open Herbivore Dung 49 4 1 4 236 -16.6 -49.27 Open Omnivore Dung 222 11 0.9821 14.982 237 -16.6 -49.27 Open Rotten Meat 17 3 1 3 238 -16.594 -49.2862 Open Herbivore Dung 25 6 0.8848 8.88 239 -16.59 -49.29 Closed Herbivore Dung 40 3 0.9762 3.487 240 -16.59 -49.29 Closed Omnivore Dung 41 5 0.9535 5.976 241 -16.59 -49.29 Closed Rotten Meat 8 5 0.6576 8.938 242 -16.59 -49.29 Open Herbivore Dung 1 1 1 1 243 -16.59 -49.29 Open Omnivore Dung 1 1 1 1 244 -16.59 -49.29 Open Rotten Meat 3 3 0.3333 5 245 -16.58 -49.25 Closed Herbivore Dung 41 7 0.9303 8.463 246 -16.58 -49.25 Closed Omnivore Dung 212 12 0.9861 12.896 247 -16.58 -49.25 Closed Rotten Meat 16 6 0.9507 6.234

223

248 -16.58 -49.25 Open Herbivore Dung 14 4 0.801 6.786 249 -16.58 -49.25 Open Omnivore Dung 132 5 0.9851 5.992 250 -16.58 -49.25 Open Rotten Meat 5 3 0.7333 3.8 251 -16.54 -49.23 Closed Herbivore Dung 119 7 0.9749 11.462 252 -16.54 -49.23 Closed Omnivore Dung 509 10 0.9941 12.994 253 -16.54 -49.23 Closed Rotten Meat 55 9 0.9461 13.418 254 -16.54 -49.23 Open Herbivore Dung 15 6 0.8091 10.2 255 -16.54 -49.23 Open Omnivore Dung 43 8 0.8851 17.767 256 -16.54 -49.23 Open Rotten Meat 1 1 1 1 257 -16.5 -56.4 Closed Omnivore Dung 16941 26 1 26 258 -16.45 -56.41 Closed Omnivore Dung 7772 24 0.9996 27 259 -16.33 -56.51 Closed Omnivore Dung 5162 27 0.9994 29.999 260 -16.33 -56.51 Open Omnivore Dung 1879 26 0.9989 27.999 261 -16.33 -49.36 Open Herbivore Dung 53 9 0.9637 9.981 262 -16.33 -49.35 Open Herbivore Dung 15 3 1 3 263 -5.86 -67.863 Closed Omnivore Dung 269 34 0.9704 39.314 264 -5.8438 -67.8355 Closed Omnivore Dung 437 21 0.9932 23.245 265 -5.8082 -67.9336 Closed Omnivore Dung 583 32 0.9915 34.496 266 -5.7688 -67.7144 Closed Omnivore Dung 339 35 0.9735 45.095 267 -5.7605 -67.9014 Closed Omnivore Dung 562 33 0.9876 39.114 268 -5.7593 -67.902 Closed Omnivore Dung 491 23 0.9898 29.237 269 -5.7550 -67.7842 Closed Omnivore Dung 342 27 0.9737 47.191 270 -5.6976 -67.7102 Closed Omnivore Dung 1031 43 0.9903 55.488 271 -5.6712 -67.7119 Closed Omnivore Dung 351 35 0.9716 44.972 272 -5.5742 -67.5003 Closed Omnivore Dung 119 21 0.9415 33.147 273 -5.5724 -67.7812 Closed Omnivore Dung 405 34 0.9828 40.11 274 -5.5433 -67.5365 Closed Omnivore Dung 343 22 0.9855 28.232 275 -5.5231 -67.6897 Closed Omnivore Dung 484 34 0.9876 38.491

224

276 -5.5128 -67.6707 Closed Omnivore Dung 918 36 0.9913 43.991 277 -5.4440 -67.2764 Closed Omnivore Dung 316 38 0.9716 44.729 278 -5.4196 -67.5232 Closed Omnivore Dung 445 25 0.9865 42.96 279 -5.4109 -67.5301 Closed Omnivore Dung 360 42 0.9751 47.77 280 -5.4062 -67.2833 Closed Omnivore Dung 258 21 0.9652 41.172 281 -5.3837 -67.1886 Closed Omnivore Dung 228 30 0.9651 37.965 282 -5.3728 -67.4143 Closed Omnivore Dung 349 25 0.98 33.143 283 -5.3133 -67.4478 Closed Omnivore Dung 594 31 0.9865 62.946 284 -5.2203 -67.342 Closed Omnivore Dung 503 29 0.9961 29.399 285 -5.1659 -67.2958 Closed Omnivore Dung 579 21 0.9914 27.239 286 -5.1529 -67.3202 Closed Omnivore Dung 574 38 0.9826 62.956 287 -5.1091 -67.1997 Closed Omnivore Dung 450 14 0.9934 16.245 288 -5.0949 -67.1273 Closed Omnivore Dung 461 12 0.9979 12.125 289 -5.0842 -67.2303 Closed Omnivore Dung 173 29 0.9308 64.792 290 -5.0455 -67.1361 Closed Omnivore Dung 149 22 0.9398 42.114 291 -4.8385 -66.9725 Closed Omnivore Dung 304 30 0.9671 54.918 292 -4.28 -70.28 Closed Omnivore Dung 699 33 1 33 293 -4.28 -70.28 Closed Omnivore Dung 249 19 1 19 294 -4.28 -70.28 Open Omnivore Dung 99 10 1 10 295 -4.28 -70.28 Open Omnivore Dung 26 7 0.926 8.923 296 -4.11 -69.93 Closed Dung and Carrion 151 18 0.9936 18.166 297 -4.11 -69.93 Closed Dung and Carrion 671 31 0.997 31.666 298 -4.1 -69.93 Closed Omnivore Dung 244 23 0.9632 33.084 299 -3.8 -70.22 Closed Omnivore Dung 1515 28 0.998 30.998 300 -3.8 -70.22 Closed Omnivore Dung 972 20 0.9979 21.998 301 -1.07 -77.91 Closed Omnivore Dung 131 13 0.9926 13.248 302 -1.07 -77.91 Closed Rotten Meat 38 9 0.9525 9.487 303 -1.07 -77.91 Open Omnivore Dung 30 8 0.9033 10.9

225

304 -1.07 -77.91 Open Rotten Meat 85 16 0.954 17.976 305 -1.07 -77.61 Closed Omnivore Dung 81 15 0.9139 39.198 306 -1.07 -77.61 Closed Rotten Meat 26 7 0.926 8.923 307 -1.05 -77.66 Open Omnivore Dung 94 13 0.9581 15.638 308 -1.05 -77.66 Open Rotten Meat 68 12 0.9418 17.912 309 -1.05 -77.66 Closed Omnivore Dung 52 6 0.9623 7.962 310 -0.96 -77.86 Open Omnivore Dung 29 8 0.9377 8.644 311 -0.96 -77.86 Closed Omnivore Dung 73 8 0.9595 10.959 312 -0.96 -77.86 Closed Rotten Meat 34 11 0.8891 12.941 313 -0.96 -77.86 Open Omnivore Dung 1 1 1 1 314 -0.96 -77.86 Open Rotten Meat 9 4 0.8025 5.778 315 -0.88 -77.78 Closed Rotten Meat 3 1 1 1 316 -0.88 -77.78 Open Omnivore Dung 1 1 1 1 317 -0.88 -77.78 Open Rotten Meat 1 1 1 1 318 -0.81 -77.78 Open Omnivore Dung 27 8 0.9312 8.963 319 -0.81 -77.78 Open Rotten Meat 20 4 0.9095 4.95 320 -0.74 -77.79 Closed Omnivore Dung 10 4 0.82 5.8 321 -0.74 -77.79 Closed Rotten Meat 1 1 1 1 322 -0.74 -77.79 Open Omnivore Dung 2 2 0.6667 2.5 323 -0.74 -77.79 Open Rotten Meat 7 2 1 2 324 -0.71 -77.75 Closed Omnivore Dung 2 1 1 1 325 -0.71 -77.75 Closed Rotten Meat 21 7 0.7677 16.524 326 -0.71 -77.75 Open Rotten Meat 3 2 0.8333 2.333 327 -0.67 -77.79 Closed Omnivore Dung 106 14 0.9626 17.962 328 -0.67 -77.79 Closed Rotten Meat 108 8 0.9911 8.248 329 -0.67 -77.79 Open Rotten Meat 2 1 1 1 330 -0.65 -77.8 Closed Omnivore Dung 36 7 0.9474 7.972 331 -0.65 -77.8 Closed Rotten Meat 131 8 1 8

226

332 -0.64 -77.85 Closed Omnivore Dung 18 6 0.8455 8.125 333 -0.64 -77.85 Closed Rotten Meat 50 7 0.9608 8.96 334 -0.64 -77.85 Open Rotten Meat 16 2 1 2 335 -0.6 -77.89 Closed Omnivore Dung 442 10 0.9955 10.998 336 -0.6 -77.89 Closed Rotten Meat 68 9 0.9567 11.217 337 -0.6 -77.89 Open Omnivore Dung 89 9 1 9 338 -0.6 -77.89 Open Rotten Meat 20 5 0.9548 5.475 339 -0.46 -77.89 Closed Omnivore Dung 83 3 1 3 340 -0.46 -77.89 Closed Rotten Meat 6 1 1 1 341 -0.46 -77.89 Open Omnivore Dung 84 3 0.9768 3.988 342 -0.46 -77.89 Open Rotten Meat 11 1 1 1 343 -0.42 -78.01 Closed Omnivore Dung 292 14 0.9932 14.997 344 -0.42 -78.01 Closed Rotten Meat 31 5 0.9698 5.484 345 -0.35 -79.71 Open Omnivore Dung 234 24 0.9658 55.863 346 -0.35 -79.71 Open Rotten Meat 34 4 0.9429 5.941 347 -0.35 -79.71 Closed Omnivore Dung 1264 38 0.9913 50.09 348 -0.35 -79.71 Closed Rotten Meat 25 11 0.6865 26.36 349 -0.35 -79.71 Closed Omnivore Dung 1159 32 0.994 44.239 350 -0.35 -79.71 Closed Rotten Meat 24 8 0.7952 19.979 351 0.13 -78.92 Open Omnivore Dung 36 8 0.946 9.944 352 0.13 -78.92 Open Rotten Meat 11 6 0.6537 13.273 353 0.13 -78.92 Closed Omnivore Dung 128 10 0.9845 11.984 354 0.13 -78.92 Closed Rotten Meat 17 8 0.7785 11.765 355 2.67 -74.17 Closed Omnivore Dung 428 19 0.9953 20.995 356 4.06 -73.04 Closed Omnivore Dung 518 15 1 15 357 4.06 -73.04 Closed Omnivore Dung 607 14 1 14 358 4.06 -73.04 Closed Omnivore Dung 1233 16 1 16 359 5.01 -76.02 Closed Omnivore Dung 6 2 1 2

227

360 5.01 -76.02 Closed Omnivore Dung 677 13 1 13 361 5.01 -76.02 Open Omnivore Dung 81 11 0.9636 13.222 362 5.02 -76.02 Closed Omnivore Dung 23 10 0.7468 18.609 363 5.02 -76.02 Closed Omnivore Dung 385 14 0.9948 14.997 364 5.02 -76.02 Open Omnivore Dung 195 8 1 8 365 5.02 -76.01 Closed Omnivore Dung 34 10 0.8564 16.066 366 5.02 -76.01 Closed Omnivore Dung 534 14 0.9963 15.996 367 5.02 -76.01 Open Omnivore Dung 229 9 1 9 368 5.03 -75.99 Open Omnivore Dung 86 7 0.9886 7.494 369 5.04 -76.99 Closed Omnivore Dung 89 15 0.967 16.483 370 5.04 -76.99 Open Omnivore Dung 30 5 0.9376 5.967 371 5.04 -76 Closed Omnivore Dung 3149 16 0.9997 16.25 372 5.04 -75.99 Closed Omnivore Dung 290 16 0.9862 21.979 373 5.04 -75.99 Closed Omnivore Dung 6081 19 0.9997 20 374 5.04 -75.99 Open Omnivore Dung 693 16 0.9942 23.988 375 5.87 -75.83 Closed Omnivore Dung 319 10 0.9969 10.498 376 5.87 -75.83 Open Omnivore Dung 163 8 1 8 377 5.87 -75.82 Open Omnivore Dung 297 11 0.9899 13.242 378 6.43 -74.56 Open Rotten Meat 16 3 1 3 379 6.43 -74.56 Open Omnivore Dung 76 4 1 4 380 6.44 -74.59 Closed Rotten Meat 91 7 0.9892 7.495 381 6.44 -74.59 Closed Omnivore Dung 468 18 0.9936 19.123 382 6.44 -74.58 Closed Rotten Meat 222 11 0.9911 11.995 383 6.44 -74.58 Closed Omnivore Dung 920 22 0.9957 27.993 384 6.44 -74.58 Closed Rotten Meat 9 5 0.7037 7.667 385 6.46 -74.59 Closed Rotten Meat 33 7 0.9118 9.909 386 6.46 -74.59 Closed Omnivore Dung 148 19 0.9803 19.745 387 6.46 -74.55 Closed Rotten Meat 169 8 0.9882 9.988

228

388 6.46 -74.55 Closed Omnivore Dung 1130 17 0.9973 21.496 389 6.46 -74.55 Open Rotten Meat 33 4 1 4 390 6.46 -74.55 Open Omnivore Dung 29 7 0.8306 16.655 391 6.46 -74.55 Closed Omnivore Dung 42 4 0.9773 4.488 392 6.47 -74.59 Closed Rotten Meat 402 16 0.9901 19.99 393 6.47 -74.59 Closed Omnivore Dung 901 20 0.9989 20.499 394 6.47 -74.59 Open Omnivore Dung 5 1 1 1 395 6.47 -74.58 Closed Rotten Meat 10 3 0.8364 3.9 396 6.47 -74.58 Closed Omnivore Dung 379 18 0.9921 19.122 397 6.47 -74.58 Open Rotten Meat 1 1 1 1 398 6.47 -74.55 Closed Omnivore Dung 268 16 0.9926 17.993 399 6.47 -74.55 Open Rotten Meat 2 1 1 1 400 6.47 -74.55 Open Omnivore Dung 1 1 1 1 401 6.49 -74.57 Open Rotten Meat 1 1 1 1 402 6.49 -74.57 Open Omnivore Dung 8 3 0.8056 3.875 403 6.5 -74.58 Open Rotten Meat 6 3 0.881 3.417 404 6.5 -74.58 Open Omnivore Dung 18 3 1 3 405 6.5 -74.57 Open Rotten Meat 14 1 1 1 406 6.5 -74.57 Open Omnivore Dung 17 3 1 3 407 6.5 -74.57 Closed Rotten Meat 2 1 1 1 408 6.5 -74.55 Closed Rotten Meat 138 13 0.9566 27.891 409 6.5 -74.55 Closed Omnivore Dung 455 16 0.9956 17.996 410 6.51 -74.55 Closed Rotten Meat 49 9 0.96 10.959 411 6.51 -74.55 Closed Omnivore Dung 69 9 0.9426 14.913 412 6.51 -74.55 Open Omnivore Dung 1 1 1 1 413 6.51 -74.55 Closed Rotten Meat 8 3 1 3 414 6.51 -74.55 Closed Omnivore Dung 2 2 0.6667 2.5 415 6.52 -74.59 Open Rotten Meat 6 1 1 1

229

416 6.52 -74.59 Open Omnivore Dung 2 1 1 1 417 6.52 -74.59 Closed Omnivore Dung 1 1 1 1 418 6.52 -74.58 Closed Rotten Meat 2 2 0.6667 2.5 419 6.52 -74.58 Closed Omnivore Dung 27 6 0.893 8.889 420 6.52 -74.58 Open Rotten Meat 24 4 0.9233 4.958 421 6.52 -74.58 Open Omnivore Dung 18 5 0.8951 6.889 422 6.52 -74.58 Closed Rotten Meat 9 4 0.8025 5.778 423 6.52 -74.58 Closed Omnivore Dung 15 2 1 2 424 6.52 -74.56 Closed Rotten Meat 105 10 0.9526 22.381 425 6.52 -74.56 Closed Omnivore Dung 237 15 0.9916 15.996 426 6.53 -74.58 Closed Rotten Meat 30 7 0.8697 12.8 427 6.53 -74.58 Closed Omnivore Dung 15 4 0.9417 4.467 428 6.53 -74.57 Closed Rotten Meat 135 15 0.9559 20.956 429 6.53 -74.57 Closed Omnivore Dung 348 19 0.9943 19.997 430 6.53 -74.56 Closed Rotten Meat 261 14 0.9847 21.969 431 6.53 -74.56 Closed Omnivore Dung 698 21 0.9971 21.666 432 6.85 -73.39 Closed Omnivore Dung 1406 24 1 24 433 6.85 -73.39 Closed Omnivore Dung 774 19 1 19 434 11.22 -74.19 Closed Omnivore Dung 5001 13 0.9998 13.25 435 11.27 -73.39 Closed Omnivore Dung 1291 19 1 19 436 11.27 -73.39 Closed Omnivore Dung 835 16 0.9988 16.25 437 13.83 -89.95 Closed Rotten Meat 625 14 0.9968 15.997 438 13.83 -89.95 Closed Omnivore Dung 3552 16 1 16 439 13.83 -89.94 Closed Rotten Meat 236 10 0.9873 14.481 440 13.83 -89.94 Closed Omnivore Dung 3564 16 0.9989 23.998 441 13.84 -89.94 Closed Rotten Meat 65 7 1 7 442 13.84 -89.94 Closed Omnivore Dung 2006 11 0.9985 13.999 443 18.34 -93.24 Closed Omnivore Dung 45 14 0.8928 17.056

230

444 18.34 -93.24 Closed Rotten Meat 11 6 0.6537 13.273 445 18.34 -93.23 Closed Omnivore Dung 105 19 0.9148 32.371 446 18.34 -93.23 Closed Rotten Meat 23 11 0.8438 12.53 447 18.34 -93.22 Closed Omnivore Dung 50 12 0.9431 13.102 448 18.35 -93.25 Closed Omnivore Dung 42 13 0.9566 13.488 449 18.35 -93.25 Closed Rotten Meat 15 3 0.8833 3.933 450 18.35 -93.24 Closed Omnivore Dung 47 13 0.852 36.979 451 18.35 -93.24 Closed Rotten Meat 26 7 0.969 7.16 452 18.35 -93.23 Closed Omnivore Dung 159 19 0.9498 46.824 453 18.35 -93.23 Closed Rotten Meat 34 11 0.8547 23.132 454 18.35 -93.22 Closed Omnivore Dung 60 12 0.9844 12.246 455 18.35 -93.22 Closed Rotten Meat 32 6 0.9738 6.161 456 18.35 -93.21 Closed Omnivore Dung 20 7 0.962 7.158 457 18.35 -93.21 Closed Rotten Meat 20 4 1 4 458 18.36 -93.25 Closed Omnivore Dung 138 13 0.9642 16.102 459 18.36 -93.25 Closed Rotten Meat 7 4 0.7551 5.714 460 18.36 -93.24 Closed Omnivore Dung 180 12 0.9779 15.978 461 18.36 -93.24 Closed Rotten Meat 7 4 0.7551 5.714 462 18.36 -93.23 Closed Omnivore Dung 70 14 0.9734 14.394 463 18.36 -93.23 Closed Rotten Meat 31 7 0.9376 8.935 464 18.36 -93.22 Closed Omnivore Dung 271 13 0.9853 20.97 465 18.36 -93.22 Closed Rotten Meat 30 4 0.9376 4.967 466 18.36 -93.21 Closed Omnivore Dung 87 17 0.9313 34.793 467 18.36 -93.21 Closed Rotten Meat 23 7 0.8312 12.739 468 18.36 -93.2 Closed Omnivore Dung 120 12 0.9669 17.95 469 18.36 -93.2 Closed Rotten Meat 30 5 0.9376 5.967 470 18.37 -93.26 Closed Omnivore Dung 183 17 0.9727 29.432 471 18.37 -93.26 Closed Rotten Meat 59 8 0.9672 8.983

231

472 18.37 -93.25 Closed Omnivore Dung 55 12 0.948 13.105 473 18.37 -93.25 Closed Rotten Meat 41 11 0.906 13.602 474 18.37 -93.24 Closed Omnivore Dung 171 16 0.9826 18.237 475 18.37 -93.24 Closed Rotten Meat 50 9 0.9016 15.125 476 18.37 -93.23 Closed Omnivore Dung 145 14 0.9863 15.986 477 18.37 -93.23 Closed Rotten Meat 68 12 0.9126 20.868 478 18.37 -93.22 Closed Omnivore Dung 128 11 0.9611 20.922 479 18.37 -93.22 Closed Rotten Meat 42 6 1 6 480 18.37 -93.21 Closed Omnivore Dung 109 11 0.9635 16.945 481 18.37 -93.21 Closed Rotten Meat 14 4 0.8762 4.929 482 18.37 -93.2 Closed Omnivore Dung 111 14 0.9551 26.387 483 18.37 -93.2 Closed Rotten Meat 16 4 1 4 484 18.38 -93.26 Closed Omnivore Dung 137 8 0.9856 8.993 485 18.38 -93.26 Closed Rotten Meat 11 5 0.8485 5.909 486 18.38 -93.25 Closed Omnivore Dung 31 7 0.9395 7.968 487 18.38 -93.25 Closed Rotten Meat 31 6 0.9376 7.935 488 18.38 -93.24 Closed Omnivore Dung 30 6 1 6 489 18.38 -93.24 Closed Rotten Meat 13 4 0.8681 4.923 490 18.38 -93.23 Closed Omnivore Dung 120 12 0.9669 17.95 491 18.38 -93.23 Closed Rotten Meat 9 4 0.8025 5.778 492 18.38 -93.22 Closed Omnivore Dung 156 17 0.968 29.42 493 18.38 -93.22 Closed Rotten Meat 28 6 0.9667 6.482 494 18.39 -93.26 Closed Omnivore Dung 22 4 0.917 4.955 495 18.39 -93.26 Closed Rotten Meat 17 4 0.9477 4.471 496 18.39 -93.25 Closed Omnivore Dung 130 17 0.9849 17.992 497 18.39 -93.25 Closed Rotten Meat 51 10 0.9035 16.127 498 18.39 -93.24 Closed Omnivore Dung 112 15 0.9648 17.643 499 18.39 -93.24 Closed Rotten Meat 23 7 0.83 14.652

232

500 18.39 -93.23 Closed Omnivore Dung 4 3 0.625 4.5 501 18.39 -93.23 Closed Rotten Meat 3 2 0.8333 2.333 502 18.4 -93.25 Closed Omnivore Dung 62 13 0.9043 21.855 503 18.4 -93.25 Closed Rotten Meat 29 5 1 5 504 18.4 -93.23 Closed Omnivore Dung 3 3 0.3333 5 505 18.6 -94.91 Closed Omnivore Dung 136 12 0.9856 12.662 506 18.6 -94.91 Closed Rotten Meat 98 6 0.9902 6.247 507 18.61 -94.92 Closed Omnivore Dung 87 9 0.9773 10.977 508 18.61 -94.92 Closed Rotten Meat 79 6 1 6 509 18.61 -94.9 Closed Omnivore Dung 200 7 1 7 510 18.61 -94.9 Closed Rotten Meat 62 4 1 4 511 18.62 -94.92 Closed Omnivore Dung 385 21 0.9896 24.99 512 18.62 -94.92 Closed Rotten Meat 261 17 0.9847 24.969 513 18.62 -94.86 Closed Omnivore Dung 136 15 0.9784 16.117 514 18.62 -94.86 Closed Rotten Meat 147 12 0.9661 24.415 515 18.63 -94.91 Closed Omnivore Dung 137 13 0.9856 13.993 516 18.63 -94.91 Closed Rotten Meat 66 8 0.9857 8.246 517 18.63 -94.87 Closed Omnivore Dung 423 11 0.9906 16.986 518 18.63 -94.87 Closed Rotten Meat 109 7 0.991 7.495 519 18.64 -94.91 Closed Omnivore Dung 207 20 0.9856 21.493 520 18.64 -94.91 Closed Rotten Meat 98 11 0.97 12.485 521 18.64 -94.9 Closed Omnivore Dung 2 1 1 1 522 19.18 -96.54 Closed Herbivore Dung 112 14 0.9826 14.661 523 19.2 -96.56 Open Herbivore Dung 92 5 1 5 524 19.2 -96.55 Open Herbivore Dung 80 6 0.9878 6.494 525 19.22 -96.5 Open Herbivore Dung 469 16 0.9915 17.996 526 19.25 -96.49 Closed Herbivore Dung 466 14 0.9979 14.166 527 19.4 -97.05 Both Herbivore Dung 249 8 0.992 9.992

233

528 19.4 -97.03 Both Herbivore Dung 88 4 1 4 529 19.41 -97.01 Both Herbivore Dung 51 7 0.9616 8.961 530 19.41 -97 Both Herbivore Dung 17 2 1 2 531 19.44 -97.02 Both Herbivore Dung 183 9 1 9 532 19.45 -97.02 Both Herbivore Dung 16 5 1 5 533 19.77 -96.46 Both Herbivore Dung 7 6 0.3304 16.714 534 19.77 -96.43 Both Herbivore Dung 10 4 0.9182 4.45 535 19.78 -96.49 Both Herbivore Dung 160 12 0.9752 15.975 536 19.78 -96.46 Both Herbivore Dung 166 13 0.9943 13.124 537 20.9 -88.44 Closed Omnivore Dung 1636 15 0.9994 15.5 538 20.91 -88.44 Closed Omnivore Dung 8882 19 0.9998 20 539 21.01 -88.2 Open Omnivore Dung 1380 20 0.9971 23.997 540 21.01 -88.19 Open Omnivore Dung 5130 25 0.9992 27.666 541 21.02 -88.47 Closed Omnivore Dung 7750 22 1 22 542 21.02 -88.46 Closed Omnivore Dung 6388 24 0.9998 24.25 543 21.02 -88.2 Open Omnivore Dung 1791 11 1 11 544 21.02 -88.19 Open Omnivore Dung 2724 18 0.9993 19 545 21.03 -87.74 Closed Omnivore Dung 2920 16 1 16 546 21.03 -87.73 Closed Omnivore Dung 7806 26 0.9996 30.499 547 21.11 -88.44 Closed Omnivore Dung 712 15 0.9958 17.996 548 21.12 -88.44 Closed Omnivore Dung 8790 26 0.9997 27.5 549 21.12 -88.43 Closed Omnivore Dung 2656 21 0.9992 22 550 21.12 -88.17 Open Omnivore Dung 8555 13 0.9998 14 551 21.13 -87.86 Open Omnivore Dung 876 18 0.9966 19.498 552 21.13 -87.85 Open Omnivore Dung 532 9 0.9981 9.499 553 21.14 -87.86 Open Omnivore Dung 1053 15 0.9972 17.997 554 21.14 -87.85 Open Omnivore Dung 748 16 0.992 30.98 555 21.15 -88.19 Open Omnivore Dung 4231 14 0.9998 14.25

234

556 21.15 -88.18 Open Omnivore Dung 1358 14 0.9993 14.25 557 21.15 -88.11 Open Omnivore Dung 4654 18 0.9998 18.5 558 21.15 -88.1 Open Omnivore Dung 3027 17 0.9987 24.997 559 21.16 -88.42 Open Omnivore Dung 7352 17 0.9997 19 560 21.16 -88.33 Open Omnivore Dung 28077 25 1 25 561 21.16 -88.32 Open Omnivore Dung 3481 19 0.9991 21.249 562 21.17 -88.42 Open Omnivore Dung 4075 22 0.9983 34.247 563 21.18 -88.17 Open Omnivore Dung 2707 9 1 9 564 21.18 -88.16 Open Omnivore Dung 3986 10 0.9995 11 565 21.21 -88.46 Open Omnivore Dung 5126 13 0.9996 14 566 21.21 -88.06 Open Omnivore Dung 19 3 1 3 567 21.21 -88.05 Open Omnivore Dung 92 6 0.9894 6.495 568 21.22 -88.06 Open Omnivore Dung 326 6 0.997 6.498 569 21.22 -88.05 Open Omnivore Dung 12457 15 1 15 570 21.28 -88.42 Open Omnivore Dung 2529 16 0.998 28.495 571 21.28 -88.41 Open Omnivore Dung 299 7 1 7 572 21.29 -88.42 Open Omnivore Dung 543 11 0.9982 11.25 573 21.29 -88.41 Open Omnivore Dung 46 4 1 4 574 21.31 -88.25 Open Omnivore Dung 1709 17 0.9982 21.497 575 21.31 -88.24 Open Omnivore Dung 802 11 0.9975 12.998 576 21.34 -88.15 Open Omnivore Dung 336 3 1 3 577 21.34 -88.14 Open Omnivore Dung 1418 8 0.9979 10.998 578 21.37 -87.76 Open Omnivore Dung 825 9 0.9964 13.495 579 21.37 -87.75 Open Omnivore Dung 977 11 0.998 12.998 580 21.38 -87.76 Open Omnivore Dung 251 6 0.9921 6.996 581 21.39 -88.52 Open Omnivore Dung 1091 9 0.9991 9.5 582 21.39 -88.51 Open Omnivore Dung 1157 12 0.9983 12.999 3

235

1 Tabela 2-2. Total GWR Summary

Valid N Mean Minimum Maximum Std.Dev. riq_scar 521 0.000 -1.959 2.127 1.000 clima1 521 0.000 -1.510 4.092 1.000 NDVI 521 0.000 -3.295 1.484 1.000 solo3 521 0.000 -3.350 3.367 1.000 mammal_S 521 0.000 -1.393 2.059 1.000 abund 521 0.000 -1.847 3.013 1.000 solo2 521 0.000 -3.659 3.256 1.000 land_cover_S 521 0.000 -4.709 2.503 1.000 clima2 521 0.000 -1.793 2.820 1.000 direct_riq_scar.clima1. 521 0.046 -0.036 0.128 0.065 direct_riq_scar.NDVI. 521 -0.009 -0.133 0.132 0.086 direct_riq_scar.solo3. 521 -0.062 -0.234 0.091 0.125 direct_riq_scar.mammal_S. 521 0.143 -0.016 0.226 0.092 direct_riq_scar.abund. 521 0.917 0.650 1.200 0.237 direct_riq_scar.solo2. 521 -0.135 -0.175 -0.106 0.017 direct_riq_scar.land_cover_S. 521 0.089 -0.092 0.159 0.081 direct_abund.clima2. 521 -0.229 -0.613 0.296 0.409 direct_abund.mammal_S. 521 -0.321 -0.705 0.033 0.298 direct_abund.solo2. 521 0.188 0.020 0.521 0.161 direct_abund.land_cover_S. 521 -0.151 -0.283 -0.080 0.076 direct_abund.NDVI. 521 0.151 -0.131 0.413 0.227 direct_mammal_S.solo3. 521 -0.033 -0.616 0.506 0.406 direct_mammal_S.land_ cover_S. 521 -0.088 -0.325 0.107 0.167 direct_mammal_S.clima2. 521 0.098 -0.510 0.633 0.371 direct_mammal_S.clima1. 521 -0.375 -0.927 0.176 0.387 indirect_riq_scar.abund.mammal_S. 521 -0.360 -0.846 0.026 0.370 indirect_riq_scar.abund.land_cover_S. 521 -0.124 -0.185 -0.073 0.035 indirect_riq_scar.abund.clima2. 521 -0.115 -0.452 0.355 0.354 indirect_riq_scar.abund.solo2. 521 0.160 0.013 0.394 0.118

236

indirect_riq_scar.abund.NDVI. 521 0.085 -0.157 0.276 0.188 indirect_abund.mammal_ S.land_cover_S. 521 -0.020 -0.075 0.023 0.038 indirect_abund.mammal_ S.clima2. 521 0.054 -0.118 0.359 0.157 indirect_abund.mammal_ S.solo3. 521 -0.106 -0.357 0.048 0.152 indirect_abund.mammal_ S.clima1. 521 0.203 -0.104 0.651 0.256 indirect_riq_scar.abund.mammal_S.land_cover_S. 521 -0.028 -0.089 0.016 0.042 indirect_riq_scar.abund. mammal_S.clima2. 521 0.077 -0.139 0.431 0.177 indirect_riq_scar.abund. mammal_S.solo3. 521 -0.130 -0.428 0.054 0.179 indirect_riq_scar.abund. mammal_S.clima1. 521 0.232 -0.120 0.781 0.312 indirect_riq_scar.mammal_S.clima2. 521 -0.016 -0.115 0.042 0.053 total_path_riq_scar.clima1 521 0.278 -0.147 0.749 0.267 total_path_riq_scar.NDVI 521 0.075 -0.191 0.350 0.216 total_path_riq_scar.solo3 521 -0.192 -0.337 0.087 0.091 total_path_riq_scar.mammal_S 521 -0.217 -0.621 0.193 0.318 total_path_riq_scar.abund 521 0.917 0.650 1.200 0.237 total_path_riq_scar.solo2 521 0.025 -0.161 0.268 0.115 total_path_riq_scar.land_cover_S 521 -0.062 -0.189 -0.014 0.048 total_path_riq_scar.clima2 521 -0.053 -0.454 0.671 0.456 total_path_abund.clima1 521 0.203 -0.104 0.651 0.256 total_path_abund.NDVI 521 0.151 -0.131 0.413 0.227 total_path_abund.solo3 521 -0.106 -0.357 0.048 0.152 total_path_abund.mammal_S 521 -0.321 -0.705 0.033 0.298 total_path_abund.solo2 521 0.188 0.020 0.521 0.161 total_path_abund.land_cover_S 521 -0.171 -0.260 -0.104 0.049 total_path_abund.clima2 521 -0.175 -0.699 0.655 0.538 total_path_mammal_S.clima1 521 -0.375 -0.927 0.176 0.387 total_path_mammal_S.solo3 521 -0.033 -0.616 0.506 0.406 total_path_mammal_S.land_cover_S 521 -0.088 -0.325 0.107 0.167 total_path_mammal_S.clima2 521 0.098 -0.510 0.633 0.371 2 3

237

4 Tabela 2-3. Amazon Region GWR Summary

Valid N Mean Minimum Maximum Std.Dev. riq_scar 163 0.134 -1.959 2.127 1.153 clima1 163 1.181 -0.854 4.092 0.688 NDVI 163 0.654 -3.033 1.484 0.549 solo3 163 -0.551 -3.350 2.637 1.181 mammal_S 163 0.022 -1.393 2.059 1.024 abund 163 0.086 -1.847 2.234 0.966 solo2 163 0.178 -1.182 3.256 0.820 land_cover_S 163 -0.405 -2.649 0.442 0.649 clima2 163 0.571 -1.793 2.820 0.991 direct_riq_scar.clima1 163 0.078 0.057 0.093 0.012 direct_riq_scar.NDVI. 163 0.105 0.076 0.132 0.013 direct_riq_scar.solo3. 163 -0.118 -0.161 -0.063 0.029 direct_riq_scar. mammal_S. 163 0.161 0.128 0.200 0.022 direct_riq_scar.abund. 163 0.773 0.753 0.780 0.005 direct_riq_scar.solo2. 163 -0.142 -0.157 -0.116 0.014 direct_riq_scar.land_ cover_S. 163 -0.026 -0.092 0.055 0.036 direct_abund.clima2. 163 -0.562 -0.600 -0.511 0.026 direct_abund.mammal_S 163 -0.001 -0.046 0.033 0.025 direct_abund.solo2. 163 0.418 0.324 0.521 0.059 direct_abund.land_ cover_S. 163 -0.128 -0.164 -0.094 0.017 direct_abund.NDVI. 163 0.300 0.263 0.360 0.019 direct_mammal_S.solo3. 163 -0.529 -0.616 -0.403 0.048 direct_mammal_ S.land_ cover_S. 163 -0.290 -0.325 -0.210 0.040 direct_mammal_S.clima2. 163 0.200 0.139 0.247 0.036 direct_mammal_S.clima1 163 0.064 -0.066 0.162 0.072 indirect_riq_scar.abund. mammal_S. 163 -0.001 -0.036 0.026 0.019 indirect_riq_scar. abund. land_cover_S. 163 -0.099 -0.128 -0.073 0.013

238 indirect_riq_ scar.abund clima2. 163 -0.434 -0.452 -0.398 0.019 indirect_riq_scar.abund. solo2. 163 0.323 0.252 0.394 0.044 indirect_riq_scar. abund. NDVI. 163 0.231 0.205 0.271 0.014 indirect_abund.mammal_S.land_cover_S. 163 -0.001 -0.011 0.010 0.007 indirect_abund. mammal_S.clima2. 163 0.000 -0.007 0.006 0.004 indirect_abund. mammal_S.solo3. 163 0.002 -0.017 0.027 0.014 indirect_abund. mammal_S.clima1. 163 0.000 -0.002 0.003 0.002 indirect_riq_scar. abund. mammal_S.land_cover_S. 163 0.000 -0.008 0.007 0.005 indirect_riq_scar.abund. mammal_S.clima2. 163 0.000 -0.005 0.005 0.003 indirect_riq_scar.abund. mammal_S.solo3. 163 0.001 -0.013 0.021 0.011 indirect_riq_scar.abund. mammal_S.clima1. 163 0.000 -0.002 0.002 0.001 indirect_riq_scar.mammal_S.clima2. 163 0.031 0.028 0.035 0.002 total_path_riq_scar.clima1 163 0.078 0.057 0.095 0.012 total_path_riq_scar.NDVI 163 0.337 0.329 0.350 0.005 total_path_riq_scar.solo3 163 -0.116 -0.166 -0.043 0.038 total_path_riq_scar. mammal_S 163 0.160 0.114 0.193 0.022 total_path_riq_scar.abund 163 0.773 0.753 0.780 0.005 total_path_riq_scar.solo2 163 0.181 0.134 0.268 0.034 total_path_riq_scar.land_ cover_S 163 -0.125 -0.189 -0.064 0.038 total_path_riq_scar.clima2 163 -0.403 -0.421 -0.374 0.015 total_path_abund.clima1 163 0.000 -0.002 0.003 0.002 total_path_abund.NDVI 163 0.300 0.263 0.360 0.019 total_path_abund.solo3 163 0.002 -0.017 0.027 0.014 total_path_abund.mammal_S 163 -0.001 -0.046 0.033 0.025 total_path_abund.solo2 163 0.418 0.324 0.521 0.059 total_path_abund.land_ cover_S 163 -0.128 -0.158 -0.104 0.014 total_path_abund.clima2 163 -0.562 -0.597 -0.517 0.023 total_path_mammal_ S.clima1 163 0.064 -0.066 0.162 0.072 total_path_mammal_ S.solo3 163 -0.529 -0.616 -0.403 0.048

239

total_path_mammal_S.land_cover_S 163 -0.290 -0.325 -0.210 0.040 total_path_mammal_ S.clima2 163 0.200 0.139 0.247 0.036 5 6 Tabela 2-4. Meso-America Region GWR Summary

Valid N Mean Minimum Maximum Std.Dev. riq_scar 140 0.237 -1.959 1.754 0.701 clima1 140 0.007 -1.029 0.337 0.378 NDVI 140 -0.484 -3.295 1.104 1.156 solo3 140 0.620 -1.117 3.367 1.022 mammal_S 140 -0.967 -1.327 1.063 0.488 abund 140 0.510 -1.641 3.013 0.998 solo2 140 0.480 -0.896 1.565 0.594 land_cover_S 140 0.376 -4.709 2.503 1.525 clima2 140 -1.144 -1.703 0.580 0.336 direct_riq_scar.clima1. 140 0.123 0.117 0.128 0.003 direct_riq_scar.NDVI. 140 -0.116 -0.133 -0.099 0.011 direct_riq_scar.solo3. 140 -0.214 -0.234 -0.197 0.008 direct_riq_scar.mammal_S. 140 -0.001 -0.016 0.024 0.011 direct_riq_scar.abund. 140 0.659 0.650 0.693 0.008 direct_riq_scar.solo2. 140 -0.140 -0.175 -0.106 0.023 direct_riq_scar.land_cover_S. 140 0.131 0.127 0.137 0.003 direct_abund.clima2. 140 -0.588 -0.613 -0.547 0.017 direct_abund.mammal_S. 140 -0.162 -0.171 -0.142 0.008 direct_abund.solo2. 140 0.066 0.020 0.165 0.028 direct_abund.land_cover_S. 140 -0.271 -0.283 -0.257 0.008 direct_abund.NDVI. 140 0.387 0.355 0.413 0.018 direct_mammal_S.solo3. 140 -0.122 -0.136 -0.107 0.006 direct_mammal_S.land_cover_S. 140 -0.138 -0.148 -0.134 0.003

240 direct_mammal_S.clima2. 140 0.542 0.431 0.633 0.049 direct_mammal_S.clima1. 140 -0.483 -0.588 -0.316 0.064 indirect_riq_scar.abund.mammal_S. 140 -0.107 -0.112 -0.093 0.005 indirect_riq_scar.abund.land_cover_S. 140 -0.179 -0.185 -0.169 0.005 indirect_riq_scar.abund.clima2. 140 -0.387 -0.400 -0.379 0.008 indirect_riq_scar.abund.solo2. 140 0.044 0.013 0.114 0.019 indirect_riq_scar.abund.NDVI. 140 0.255 0.233 0.276 0.012 indirect_abund.mammal_S.land_cover_S. 140 0.022 0.020 0.023 0.001 indirect_abund.mammal_S.clima2. 140 -0.088 -0.091 -0.067 0.005 indirect_abund.mammal_S.solo3. 140 0.020 0.017 0.020 0.001 indirect_abund.mammal_S.clima1. 140 0.078 0.049 0.084 0.008 indirect_riq_scar.abund.mammal_S.land_cover_S. 140 0.015 0.013 0.016 0.001 indirect_riq_scar.abund.mammal_S.clima2. 140 -0.058 -0.060 -0.046 0.003 indirect_riq_scar.abund.mammal_S.solo3. 140 0.013 0.012 0.013 0.000 indirect_riq_scar.abund.mammal_S.clima1. 140 0.051 0.034 0.056 0.005 indirect_riq_scar.mammal_S.clima2. 140 -0.001 -0.010 0.010 0.006 total_path_riq_scar.clima1 140 0.174 0.152 0.176 0.005 total_path_riq_scar.NDVI 140 0.138 0.100 0.171 0.023 total_path_riq_scar.solo3 140 -0.201 -0.222 -0.185 0.008 total_path_riq_scar.mammal_S 140 -0.108 -0.114 -0.084 0.008 total_path_riq_scar.abund 140 0.659 0.650 0.693 0.008 total_path_riq_scar.solo2 140 -0.096 -0.161 -0.007 0.039 total_path_riq_scar.land_cover_S 140 -0.033 -0.044 -0.019 0.008 total_path_riq_scar.clima2 140 -0.446 -0.454 -0.415 0.007 total_path_abund.clima1 140 0.078 0.049 0.084 0.008 total_path_abund.NDVI 140 0.387 0.355 0.413 0.018 total_path_abund.solo3 140 0.020 0.017 0.020 0.001 total_path_abund.mammal_S 140 -0.162 -0.171 -0.142 0.008 total_path_abund.solo2 140 0.066 0.020 0.165 0.028

241

total_path_abund.land_cover_S 140 -0.249 -0.260 -0.236 0.008 total_path_abund.clima2 140 -0.676 -0.699 -0.614 0.018 total_path_mammal_S.clima1 140 -0.483 -0.588 -0.316 0.064 total_path_mammal_S.solo3 140 -0.122 -0.136 -0.107 0.006 total_path_mammal_S.land_cover_S 140 -0.138 -0.148 -0.134 0.003 total_path_mammal_S.clima2 140 0.542 0.431 0.633 0.049 7 8 Tabela 2-5. Subtropical formations region GWR Summary

Valid N Mean Minimum Maximum Std.Dev. riq_scar 218 -0.252 -1.959 2.082 0.988 clima1 218 -0.887 -1.510 -0.224 0.336 NDVI 218 -0.178 -2.611 1.340 0.901 solo3 218 0.014 -1.936 1.092 0.458 mammal_S 218 0.605 -0.198 1.661 0.699 abund 218 -0.392 -1.847 2.756 0.857 solo2 218 -0.441 -3.659 2.813 1.140 land_cover_S 218 0.062 -1.619 0.957 0.612 clima2 218 0.308 -1.286 1.739 0.638 direct_riq_scar.clima1. 218 -0.027 -0.036 -0.001 0.005 direct_riq_scar.NDVI. 218 -0.026 -0.034 -0.009 0.006 direct_riq_scar.solo3. 218 0.078 0.033 0.091 0.013 direct_riq_scar.mammal_S. 218 0.222 0.201 0.226 0.006 direct_riq_scar.abund. 218 1.191 1.110 1.200 0.015 direct_riq_scar.solo2. 218 -0.126 -0.145 -0.120 0.007 direct_riq_scar.land_cover_S. 218 0.148 0.141 0.159 0.006 direct_abund.clima2. 218 0.250 -0.009 0.296 0.055 direct_abund.mammal_S. 218 -0.663 -0.705 -0.470 0.045 direct_abund.solo2. 218 0.095 0.076 0.205 0.025

242 direct_abund.land_cover_S. 218 -0.090 -0.133 -0.080 0.009 direct_abund.NDVI. 218 -0.113 -0.131 0.015 0.025 direct_mammal_S.solo3. 218 0.394 -0.103 0.506 0.140 direct_mammal_S.land_cover_S. 218 0.094 0.020 0.107 0.012 direct_mammal_S.clima2. 218 -0.264 -0.510 0.201 0.247 direct_mammal_S.clima1. 218 -0.635 -0.927 0.176 0.364 indirect_riq_scar.abund.mammal_S. 218 -0.790 -0.846 -0.522 0.062 indirect_riq_scar.abund.land_cover_S. 218 -0.107 -0.147 -0.096 0.009 indirect_riq_scar.abund.clima2. 218 0.298 -0.010 0.355 0.068 indirect_riq_scar.abund.solo2. 218 0.112 0.091 0.228 0.028 indirect_riq_scar.abund.NDVI. 218 -0.135 -0.157 0.017 0.030 indirect_abund.mammal_S.land_cover_S. 218 -0.063 -0.075 -0.010 0.010 indirect_abund.mammal_S.clima2. 218 0.185 -0.118 0.359 0.164 indirect_abund.mammal_S.solo3. 218 -0.267 -0.357 0.048 0.101 indirect_abund.mammal_S.clima1. 218 0.435 -0.104 0.651 0.248 indirect_riq_scar.abund.mammal_S.land_cover_S. 218 -0.075 -0.089 -0.011 0.012 indirect_riq_scar.abund.mammal_S.clima2. 218 0.222 -0.139 0.431 0.195 indirect_riq_scar.abund.mammal_S.solo3. 218 -0.320 -0.428 0.054 0.121 indirect_riq_scar.abund.mammal_S.clima1. 218 0.521 -0.120 0.781 0.297 indirect_riq_scar.mammal_S.clima2. 218 -0.060 -0.115 0.042 0.055 total_path_riq_scar.clima1 218 0.494 -0.147 0.749 0.295 total_path_riq_scar.NDVI 218 -0.161 -0.191 0.008 0.036 total_path_riq_scar.solo3 218 -0.242 -0.337 0.087 0.108 total_path_riq_scar.mammal_S 218 -0.568 -0.621 -0.321 0.056 total_path_riq_scar.abund 218 1.191 1.110 1.200 0.015 total_path_riq_scar.solo2 218 -0.014 -0.031 0.086 0.025 total_path_riq_scar.land_cover_S 218 -0.034 -0.044 -0.014 0.007 total_path_riq_scar.clima2 218 0.461 -0.075 0.671 0.202 total_path_abund.clima1 218 0.435 -0.104 0.651 0.248

243

total_path_abund.NDVI 218 -0.113 -0.131 0.015 0.025 total_path_abund.solo3 218 -0.267 -0.357 0.048 0.101 total_path_abund.mammal_S 218 -0.663 -0.705 -0.470 0.045 total_path_abund.solo2 218 0.095 0.076 0.205 0.025 total_path_abund.land_cover_S 218 -0.153 -0.159 -0.139 0.003 total_path_abund.clima2 218 0.435 -0.104 0.655 0.214 total_path_mammal_S.clima1 218 -0.635 -0.927 0.176 0.364 total_path_mammal_S.solo3 218 0.394 -0.103 0.506 0.140 total_path_mammal_S.land_cover_S 218 0.094 0.020 0.107 0.012 total_path_mammal_S.clima2 218 -0.264 -0.510 0.201 0.247 9

244

1 Capítulo 3 Supplementary Information S3 2 Tabela 3-1. Dung beetle collected in Forest and Pasture in Cerrado. F = Forest, P = Pasture.

Goiânia 1 Goiânia 2 Goiânia 3 Goiânia 4 Leopoldo 1 Leopoldo 2 Silvânia 1 Habitats F P F P F P F P F P F P F P Agamopus viridis 0 0 0 1 0 2 0 2 0 0 0 0 0 0 Ateuchus aff. pruneus 0 0 0 0 0 0 0 0 0 0 3 0 0 0 Ateuchus vividus 0 0 0 0 0 0 0 0 0 0 0 0 1 0 Canthon aff. piluliformis 0 0 0 0 0 1 0 0 0 0 1 12 0 0 Canthon curvodilatatus 0 1 0 1 0 0 0 0 0 0 0 0 0 0 Canthon lituratus 0 0 0 23 0 58 0 26 0 0 0 31 0 0 Canthon sp. 0 0 0 0 0 0 0 0 0 1 0 0 0 0 Canthidium aff. barbacenicum 0 0 0 3 0 0 0 2 0 7 0 2 0 0 Canthidium aff. lucidum 0 0 0 1 0 0 1 0 0 0 0 0 0 0 Canthidium sp.1 68 0 137 0 27 0 536 0 26 0 0 0 22 0 Canthidium sp.2 0 0 0 0 0 0 1 1 0 0 0 0 0 0 Canthidium sp.3 0 0 0 0 0 0 0 0 0 0 0 0 1 0 Canthonela sp. 0 0 0 1 0 0 2 1 0 0 0 3 0 0 Coprophanaeus cyanescens 6 0 5 0 0 0 15 0 0 0 0 0 1 0 Coprophanaeus ensifer 0 0 0 0 2 0 0 0 0 0 0 0 0 0 Coprophanaeus spitzi 0 0 0 0 0 1 0 0 0 0 0 0 0 0 Deltochilum enceladus 0 0 0 0 1 0 3 0 5 0 0 0 1 0 Deltochilum sextuberculatum 1 0 1 0 6 0 21 0 6 0 1 0 0 0 Deltochilum sp. 1 0 17 0 2 0 9 0 3 0 0 0 57 0 Dendropaemon nitidicolis 0 0 0 0 0 0 0 0 0 0 0 1 0 0 Dichotomius aff. carbonarius 0 0 0 1 3 0 1 0 16 0 0 0 2 0 Dichotomius aff. zicani 1 0 0 0 10 0 1 0 5 0 0 0 2 3 Dichotomius angeloi 0 0 0 0 0 0 0 0 1 0 0 0 0 0

245

Dichotomius bos 0 1 0 23 1 2 0 1 0 1 0 0 0 0 Dichotomius cuprinus 0 0 0 0 2 0 2 0 0 0 0 0 0 0 Dichotomius nisus 0 0 0 5 0 0 0 0 0 0 1 29 0 0 Dichotomius sp.1 0 0 0 0 0 0 0 0 0 0 0 0 3 0 Dichotomius sp.2 0 0 0 0 0 0 0 0 0 0 0 0 1 0 Dichotomius transiens 0 0 0 0 1 0 3 0 1 0 0 0 1 0 Digitonthophagus sp. 0 1 0 26 0 0 0 0 0 0 0 0 0 0 Eurysternus caribaeus 0 0 3 0 1 0 10 0 17 0 0 0 16 0 Eurysternus nigrovirens 0 0 0 0 0 0 0 0 0 0 20 0 0 0 Eutrichillum hirsutum 2 1 1 1 7 1 0 0 0 1 2 3 0 0 Isocopris inhiatus 0 0 0 0 0 0 0 1 0 0 0 0 0 0 Onthophagus buculus 0 0 0 2 4 4 0 11 0 0 0 7 0 0 Onthophagus ptox 0 0 75 0 189 0 42 0 13 0 5 1 2 0 Ontherus appendiculatus 0 0 0 0 0 0 0 0 0 0 0 0 1 0 Ontherus asteca 0 0 0 0 2 0 0 0 0 0 0 0 0 0 Trichillum adjuntum 0 0 0 0 0 0 0 0 0 0 1 5 0 0 Trichillum externepunctatum 3 1 0 200 11 82 1 13 0 0 0 0 0 0 Trichillum heydeni 0 0 0 0 0 0 0 1 0 0 0 0 0 0 Uroxys aff. epipleuralis 7 0 3 0 0 0 35 0 10 0 0 0 2 0 3 4 5 6 7 8

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9 Tabela 3-2. Dung beetle collected in Forest and Pasture in Atlantic Forest. F = Forest, P = Pasture.

Arraial 1 Arraial 2 Belchior1 Belchior2 Ilhota 1 Ilhota 2 Indaial Habitats F P F P F P F P F P F P F P Canthon aff. luctuosos 0 0 0 0 0 0 0 0 0 0 0 0 1 0 Canthon coloratus 1 0 0 0 0 0 0 0 0 0 0 0 0 0 Canthon conformis 0 0 0 0 0 2 0 0 0 0 0 0 0 0 Canthon podagricus 0 0 0 0 0 0 0 0 0 4 0 0 1 0 Canthon rutilans cyanescens 7 0 2 0 16 1 11 1 75 0 41 2 77 0 Canthidium aff. trinodosum 0 0 0 0 0 0 0 0 0 0 5 0 1 0 Coprophanaeus bellicosus 0 0 0 0 0 0 1 0 0 0 0 0 0 0 Coprophanaeus cerberus 0 0 1 0 0 0 0 0 0 0 0 0 0 0 Coprophanaeus dardanus 9 0 3 0 19 5 39 3 6 0 0 0 0 0 Coprophanaeus saphirinus 2 0 3 0 1 0 0 0 0 0 4 0 0 0 Deltochilum brasilense 1 0 4 0 0 0 2 0 1 0 5 0 0 0 Deltochilum furcatum 0 0 15 0 4 0 5 0 1 0 3 0 0 0 Deltochilum morbilossum 0 0 1 0 0 0 0 0 8 0 2 0 0 0 Deltochilum multicolor 1 0 6 0 8 2 1 6 7 10 2 8 1 0 Dichotomius ascanius 0 0 6 0 0 0 4 0 2 0 0 0 0 0 Dichotomius mormom 2 0 6 0 0 0 0 0 1 0 0 0 0 0 Dichotomius quadrinodosus 0 0 1 0 0 0 0 0 0 0 0 0 0 0 Dichotomius sericeus 2 0 12 0 0 0 6 0 0 0 0 0 0 0 Eurysternus paralelus 0 0 6 0 7 0 5 5 0 0 0 0 1 0 Onthophagus aff. hematopus 0 0 18 0 2 0 0 0 0 0 0 0 0 0 Phaneus splendidulus 1 0 3 0 0 0 0 0 0 0 0 0 0 0 10 11 12

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13 Tabela 3-3. Dung beetle traits from individuals collected in Cerrado. Vol = Volume, Len = Length, W.Lo = Wing Load, Ps.H = Prosternum Height, Me.L = Metatibia Length, Pt.A 14 = Protibia Area, Ey.A = Eye Dorsal Area, He.W = Head Width, He.L = Head Length, Pr.W = Pronotum Width, Le.S = Levins Standardized Index, Nes = Nest, Pe.B = Pear/Ball 15 Nest, Di.A = Diel Activity, Ho.D = Horizontal Displacement.

Specie Pr.W He.L He.W Ey.A Pt.A Me.L Ps.H W.Lo Len. Vol. Le.S Nes Pe.B Di.A Ho.D Agamopus viridis 2.44 0.84 1.51 0.03 0.28 1.22 1.14 3.40 3.89 10.87 0.46 NA NA nocturnal no Ateuchus aff. pruneus 4.36 1.21 2.86 0.08 0.89 1.56 2.45 4.60 7.46 79.94 0.00 NA NA nocturnal no Ateuchus vividus 4.40 1.66 2.54 0.02 0.50 1.44 2.62 3.19 6.35 73.03 0.00 NA NA nocturnal no Canthon aff. 3.27 0.78 1.83 0.02 0.42 1.52 1.92 2.19 4.55 29.39 0.08 yes yes diurnal yes piluliformis Canthon 3.42 0.66 1.97 0.01 0.49 1.67 1.80 2.33 4.64 28.52 0.50 yes yes diurnal yes curvodilatatus Canthon lituratus 2.95 0.90 1.71 0.03 0.44 1.89 1.62 1.81 4.39 21.25 0.09 yes yes diurnal yes Canthon sp. 3.41 0.81 1.72 0.01 0.42 1.96 1.60 2.40 4.92 26.79 0.00 yes yes diurnal yes Canthidium aff. 3.25 0.95 2.04 0.78 0.40 1.44 1.87 3.12 5.01 31.04 0.08 yes yes diurnal no barbacenicum Canthidium aff. 5.19 1.04 2.91 0.02 0.81 1.88 2.38 3.26 6.22 76.17 0.50 yes yes diurnal no lucidum Canthidium sp.1 2.39 0.66 1.47 0.03 0.50 0.97 1.47 2.43 3.70 12.91 0.25 yes yes diurnal no Canthidium sp.2 2.19 0.91 1.33 0.04 0.24 1.29 1.27 3.16 3.41 9.47 0.00 yes yes diurnal no Canthidium sp.3 4.56 1.11 3.19 0.09 0.82 1.92 3.40 3.34 7.78 120.78 0.00 yes yes diurnal no Canthonela sp. 2.30 0.91 1.48 0.03 0.20 1.00 1.21 3.91 3.78 10.65 0.41 NA NA NA NA Coprophanaeus 14.92 3.12 8.39 0.91 11.91 5.37 8.70 9.01 19.68 2803.43 0.46 yes yes nocturnal no cyanescens Coprophanaeus 14.70 3.89 7.71 0.89 28.14 10.20 15.54 40.27 20.12 4650.92 0.00 yes yes nocturnal no ensifer Coprophanaeus spitzi 14.81 4.92 9.26 1.44 5.46 6.49 9.45 11.81 19.22 2688.46 0.00 yes yes nocturnal no Deltochilum enceladus 18.35 4.51 10.64 1.76 11.84 15.11 10.00 14.76 32.09 5865.57 0.11 yes yes nocturnal yes Deltochilum 5.85 1.55 3.38 0.16 3.36 3.49 7.78 4.71 10.62 448.50 0.39 yes yes nocturnal yes sextuberculatum Deltochilum sp. 7.01 1.97 3.81 0.15 1.64 4.33 4.10 5.22 12.30 365.89 0.76 yes yes nocturnal yes

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Dendropaemon 4.10 1.26 3.08 0.16 0.93 2.06 1.98 2.36 7.91 64.17 0.00 NA NA diurnal NA nitidicolis Dichotomius aff. 9.41 2.79 5.82 0.48 18.52 4.24 5.68 8.45 14.01 774.68 0.15 yes no nocturnal no carbonarius Dichotomius aff. zicani 12.86 4.30 7.80 1.15 9.56 4.94 9.31 13.45 17.42 2355.86 0.30 yes no nocturnal no Dichotomius angeloi 9.93 2.64 6.36 0.59 5.01 4.94 6.97 9.06 15.47 1070.70 0.00 yes no nocturnal no Dichotomius bos 13.35 4.71 8.55 0.91 9.22 5.12 8.28 12.38 18.02 2027.94 0.40 yes no nocturnal no Dichotomius cuprinus 9.57 2.27 6.09 0.44 3.74 4.30 4.95 10.63 14.36 691.38 0.50 yes no nocturnal no Dichotomius nisus 12.76 3.53 7.83 0.81 8.63 5.36 7.60 13.05 17.96 1820.41 0.21 yes no nocturnal no Dichotomius sp.1 9.24 2.89 5.77 0.68 4.02 3.96 5.71 8.80 13.28 777.63 0.00 yes no nocturnal no Dichotomius sp.2 7.14 2.08 4.22 0.19 2.40 3.71 4.70 6.30 10.36 347.52 0.00 yes no nocturnal no Dichotomius transiens 10.78 2.92 6.30 0.56 5.06 4.43 6.67 9.11 14.81 1108.38 0.40 yes no nocturnal no Digitonthophagus sp. 6.39 2.12 3.56 0.13 2.04 2.53 3.32 4.73 9.07 194.49 0.04 yes yes diurnal yes Eurysternus caribaeus 7.02 2.04 4.10 0.08 2.06 4.50 4.36 3.87 14.37 446.44 0.12 yes yes mixed no Eurysternus 2.88 1.15 1.90 0.03 0.33 2.41 1.73 2.21 5.93 29.87 0.46 yes yes mixed no nigrovirens Eutrichillum hirsutum 2.12 0.47 1.16 0.01 0.23 0.85 1.35 2.83 3.53 10.15 0.50 yes yes mixed no Isocopris inhiatus 17.91 5.54 9.71 0.64 13.52 7.65 11.07 21.79 23.70 4696.53 0.00 yes no nocturnal no Onthophagus buculus 3.65 1.36 2.14 0.04 0.53 1.29 1.93 2.21 4.61 30.10 0.21 yes yes mixed no Onthophagus ptox 2.88 0.97 1.72 0.04 0.37 1.15 1.65 2.15 4.31 21.03 0.15 yes no mixed no Ontherus 6.00 2.22 4.94 0.35 2.30 2.66 4.03 8.19 10.47 252.95 0.00 yes no nocturnal no appendiculatus Ontherus asteca 6.51 1.59 4.45 0.23 2.47 2.65 3.82 7.66 10.00 251.24 0.00 yes yes nocturnal no Trichillum adjuntum 2.36 0.66 1.30 0.02 0.24 1.10 1.09 3.86 3.78 10.13 0.19 yes yes diurnal no Trichillum 2.00 0.39 1.17 0.01 0.17 0.74 1.24 3.26 3.20 7.84 0.33 no no nocturnal no externepunctatum Trichillum heydeni 2.37 0.46 1.56 0.02 0.22 0.87 1.17 3.03 3.49 9.67 1.00 no no nocturnal no Uroxys aff. epipleuralis 2.03 0.51 1.17 0.02 0.16 0.96 1.00 2.52 3.29 7.19 0.30 no no nocturnal no 16 17

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18 Tabela 3-4. Dung beetle traits from individuals collected in Atlantic Forest. Vol = Volume, Len = Length, W.Lo = Wing Load, Ps.H = Prosternum Height, Me.L = Metatibia Length, 19 Pt.A = Protibia Area, Ey.A = Eye Dorsal Area, He.W = Head Width, He.L = Head Length, Pr.W = Pronotum Width, Le.S = Levins Standardized Index, Nes = Nest, Pe.B = Pear/Ball 20 Nest, Di.A = Diel Activity, Ho.D = Horizontal Displacement. Species Pr.W He.L He.W Ey.A Pt.A Me.L Ps.H W.Lo Len. Vol. Le.S Nes Pe.B Di.A Ho.D Canthon aff. 4.23 1.12 2.13 0.06 0.52 2.67 2.10 3.02 6.49 57.56 0.00 yes yes diurnal yes luctuosos Canthon coloratus 4.31 1.79 2.26 0.09 0.99 2.52 2.71 3.41 6.61 77.02 0.00 yes yes diurnal yes Canthon conformis 4.78 0.67 2.41 0.01 0.69 2.52 2.39 3.06 6.80 77.91 0.00 yes yes diurnal yes Canthon podagricus 3.82 0.71 1.96 0.02 0.58 2.10 2.03 2.55 5.79 46.03 0.00 yes yes diurnal yes Canthon rutilans 6.36 1.88 3.49 0.07 2.09 3.79 3.49 4.20 9.01 203.00 0.00 yes yes diurnal yes cyanescens Canthidium aff. 3.03 0.52 1.72 0.02 0.41 1.19 1.80 2.79 4.63 25.77 0.00 yes yes diurnal yes trinodosum Coprophanaeus 21.76 4.69 11.44 1.82 18.71 5.48 10.79 13.90 27.79 6525.40 0.00 yes yes nocturnal no bellicosus Coprophanaeus 13.61 3.67 8.33 0.36 10.29 4.47 8.88 7.67 18.49 2233.91 0.00 yes yes nocturnal no cerberus Coprophanaeus 15.81 4.37 9.28 1.13 11.38 5.72 8.80 8.91 20.71 2939.41 0.40 yes yes nocturnal no dardanus Coprophanaeus 11.28 3.04 6.16 0.61 5.57 4.35 7.23 6.70 15.42 1312.14 0.24 yes yes nocturnal no saphirinus Deltochilum 12.99 3.30 7.80 1.13 6.29 12.25 6.39 11.08 21.68 1821.80 0.37 yes yes nocturnal yes brasilense Deltochilum 10.58 2.32 5.77 0.46 4.24 6.85 5.78 7.32 18.67 1132.05 0.26 yes yes nocturnal yes furcatum Deltochilum 6.56 1.85 3.83 0.09 1.19 4.50 3.36 4.50 11.05 244.75 0.10 yes yes nocturnal yes morbilossum Deltochilum 8.74 1.99 4.97 0.25 2.56 6.20 4.72 5.19 15.55 644.95 0.35 yes yes nocturnal yes multicolor Dichotomius 7.55 2.28 4.74 0.38 2.92 3.20 4.19 7.18 11.38 422.97 0.47 yes no nocturnal no ascanius

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Dichotomius 13.44 5.81 8.41 1.62 9.10 5.09 9.80 16.11 18.20 2472.75 0.40 yes no nocturnal no mormom Dichotomius 17.17 6.34 10.84 1.28 11.85 6.69 13.65 18.17 22.52 5275.31 0.00 yes no nocturnal no quadrinodosus Dichotomius 8.37 3.08 5.51 0.43 3.66 3.60 4.55 8.29 12.64 500.27 0.46 yes no nocturnal no sericeus Eurysternus 5.21 1.56 3.44 0.08 1.29 3.94 3.21 3.22 11.13 186.63 0.25 yes yes mixed no paralelus Onthophagus aff. 3.09 1.01 1.78 0.05 0.45 1.24 1.81 2.51 4.75 26.79 0.24 yes no mixed no hematopus Phaneus 11.40 4.30 6.97 0.66 6.74 4.28 6.87 5.97 15.17 1182.44 0.50 yes yes diurnal no splendidulus 21 22

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1 2 Figure S3.1. CWM of Dung Beetles for traits measured in forest patches and pasture for Goiânia and Atlantic 3 Forest Region. In red Forest, in Blue Pasture 4

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11 Figure S3.2. Relation between Dung Beetles Species Richness and Trait Statistic (T_IP.IC) for traits 12 measured in forest patches and pasture for Goiânia and Atlantic Forest Region. Vol = Volume, Len = Length, 13 W.Lo = Wing Load, Ps.H = Prosternum Height, Me.L = Metatibia Length, Pt.A = Protibia Area, Ey.A = Eye 14 Dorsal Area, He.W = Head Width, He.L = Head Length, Pr.W = Pronotum Width. T_IP.IC=Internal Filtering 15 of Individuals.

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