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INSTITUTO NACIONAL DE PESQUISAS DA AMAZÔNIA – INPA
PROGRAMA DE PÓS-GRADUAÇÃO EM ECOLOGIA
Uso de classes topográficas para descrever habitat de aves
florestais amazônicas
MAÍRA REMONATTO RIZZI
Manaus, Amazonas
Outubro, 2013
MAÍRA REMONATTO RIZZI
Orientadora: Dra. Marina Anciães
Co-orientador: Dr. Mario Cohn-Haft
Manaus, Amazonas
Outubro, 2013
Uso de classes topográficas para descrever habitat de
aves florestais amazônicas
Dissertação apresentada ao Instituto
Nacional de Pesquisas da Amazônia,
como parte dos requisitos para
obtenção do título de Mestre em
Biologia (Ecologia).
i
Banca examinadora do trabalho escrito
Dr. Victor Lemes Landeiro
(Universidade Federal do Mato Grosso)
Aprovada com correções
Dr. Luciano Nicolas Naka
(Universidade Federal de Pernambuco)
Aprovada com correções
Banca examinadora da defesa oral pública
Dr. Erik Johnson
(Audubon Society)
Dr. Marcelo Menin
(Universidade Federal do Amazonas)
Dr. Fabricio Beggiato Baccaro
(Universidade Federal do Amazonas)
Aprovada por unanimidade
ii
R627 Rizzi, Maíra Remonatto Uso de classes topográficas para descrever habitat de aves
florestais amazônicas. / Maíra Remonatto Rizzi. --- Manaus : [s.n],
2013.
vi, 53 f. : il. color.
Dissertação (Mestrado) --- INPA, Manaus, 2013.
Orientador : Marina Anciães.
Coorientador : Mario Cohn-Haft.
Área de concentração : Ecologia.
1. Aves neotropicais. 2. Aves - Habitat. 3. Aves – Distribuição geográfica. I. Título.
CDD 598.29
Sinopse
Avaliamos o uso das classes topográficas baixio, vertente e platô, para descrever habitat por aves
florestais amazônicas, em seis parcelas de floresta de terra firme ao Norte de Manaus. Foi utilizada a
abordagem de uso de ambientes versus sua disponibilidade. Os dados de registro de aves foram
provenientes do banco de dados de aves do Projeto TEAM, sendo as áreas classificadas nos três
ambientes utilizando dados e ferramentas de SIG.
Palavras-chave: Amazônia, Ponto de escuta, avifauna, topografia, SIG, Reserva Ducke, PDBFF, ZF-
2.
iii
Agradecimentos
À minha orientadora, Dra Marina Anciães, pela orientação e confiança desde que cheguei em
Manaus, muito obrigada pela compreensão e apoio.
Ao Dr. Mario Cohn-Haft, co-orientador, pelo acolhimento e por toda a dedicação na
construção desta ideia.
Ao Projeto TEAM, pelos dados, e ao Christian B. Andretti, pelo incentivo e entusiasmo em
desenvolver a parceria.
Ao Instituto Nacional de Pesquisas da Amazônia e à Pós-graduação de Ecologia.
Ao CNPq pela concessão da bolsa de estudos.
Às amigas e companheiras, panteras, Nayara Tartari Soto e Stéphany Wátzel, pelo imenso
companheirismo e ajuda durante todo o processo. Obrigada pela paciência, ajuda, e parceria.
Obrigada aos amigos de todas as horas: Julia V. Tavares, Carolina Freitas, Juliana
Bonannomi, Francisco C. Diniz, Gabriel McCrate, Thiago Belissário. Obrigada aos amigos e
colegas de turma de Ecologia 2011.
À minha família, por ter me incentivado e apoiado em cada momento, pela compreensão em
todo o processo, e que mesmo a 3.000km de distância, estiveram por perto. Obrigada mãe
(Isete C. R. Rizzi), pai (Nivaldo E. Rizzi) e irmão (Solo R. Rizzi), por todo carinho, visitas,
paciência e amor.
Ao Renato de S. P. Lemgruber, querido parceiro e companheiro de tudo. Obrigada pela
paciência, ajuda, alegrias e grandes momentos proporcionados durante todo esse processo e
muitos outros.
iv
Resumo Baixios, vertentes e platôs são ambientes que agregam características de solo e vegetação
distintas, importantes para a seleção de habitat em aves, podendo ser reconhecidos como
habitats pelas espécies da avifauna amazônica. Para testar se espécies de aves florestais
amazônicas selecionam, são especializadas em, ou evitam algum desses ambientes, utilizamos
a abordagem de uso dessas classes topográficas versus sua disponibilidade. O ambiente
disponível correspondeu à frequência de cada classe topográfica em 600 ha de floresta
primária ao norte de Manaus, que foram classificadas utilizando valores de altitude e
declividade através de ferramentas de SIG. Baixios foram áreas com baixa altitude, já
vertentes e platôs, em altitude acima do definido para baixio, foram separados usando
declividade. Os dados de aves, pertencentes ao Projeto TEAM foram coletados através de
censos por pontos de escuta em seis parcelas de 100 ha, visitadas de uma a quatro vezes por
ano, ao longo de cinco anos. Essas seis parcelas continham, cada uma, 36 pontos distantes
entre si 200 m, totalizando 216 pontos amostrados. Vertente foi o ambiente mais abundante
(47%), seguido de platô (33%) e baixio (20%). Das 160 espécies analisadas, 50 (31%)
selecionaram pelo menos um ambiente, nenhuma foi especializada e 16 (32%) evitaram
baixio ou platô, sendo vertente nunca evitada pelas espécies. Nossos resultados mostram que,
para algumas espécies típicas de igarapés, a classe topográfica “baixio” foi selecionada,
estando de acordo com as descrições de habitat na literatura ornitológica. Para outras espécies,
as associações com as classes topográficas representam novas hipóteses que devem ser
investigadas em maior detalhe. O conhecimento prévio de história natural sugere que a
relação entre as categorias topográficas e requisitos específicos de habitat é indireta, e o uso
dessas classes para descrever o habitat não deve sobrepujar descrições mais detalhadas de
micro-habitat. Nosso trabalho representou uma tentativa de quantificar o uso do ambiente
versus sua disponibilidade com o intuito de encontrar uma classificação funcional e mais
refinada de habitat para aves dentro da floresta de terra firme, o que proporcionará uma base
de comparação para futuros estudos sobre o uso de habitats pelas aves na Amazônia.
PALAVRAS - CHAVE: Aves Neotropicais, relevo, SIG, pontos de escuta, floresta de terra-
firme.
v
Abstract
Use of topographic classes to describe habitat of Amazon forest birds
Bottomlands, slopes, and plateaus are landscape classes that aggregate distinct characteristics
of soil and vegetation, which in turn are important for habitat selection by birds, making these
classes potentially recognizable as habitats for Amazon bird species. To test whether species
of terra firme forest birds select, are specialized in, or avoid any of these environments, we
use the approach of the use of topographic classes versus its availability. Available
environment corresponded to the frequency of each class in 600 ha of primary forest north of
Manaus, Brazil, classified using height and percent of slope with GIS tools. Bottomlands were
the lowest areas, and slopes and plateaus were defined by percent of slope above elevation
limit for bottomlands. Bird data, from the TEAM Project database, were obtained in point
count censuses in six 100-ha plots visited one to four times per year, for five years. These six
plots comprised 36 points, each one, separated 200 m apart from each other, for a total of 216
sampled points. Slope was the most abundant class (47%), followed by plateaus (33%) and
bottomlands (20%). In 160 species analyzed, 50 (31%) selected topographical classes, none of
them were specialized and 16 (32%) avoided bottomland or plateau, but never slope. Our
results showed that for a few species typical of forest streams, the topographic class
“bottomland” represents preferred habitat. For other species, these topographical habitat
associations represent novel hypotheses and should be investigated in greater detail. In
general, previous natural history knowledge of these species suggests that the relationship
between topographical category and specific habitat requirements are indirect, and the use of
these classes to describe habitat should also consider relevant micro-habitat features for birds.
Finally, our work represented an attempt to quantify the use of environment versus its
availability to find out a more refined and functional habitat classification for birds within the
terra firme forest, providing comparison base for further studies about habitat use by birds in
the Amazon.
KEYWORDS: Neotropical birds, relief, GIS, point-counts, “terra-firme” forest.
vi
Sumário
Resumo....................................................................................................................................iii
Abstract..................................................................................................................... ...............iv
Introdução.................................................................................................................................1
Objetivos...................................................................................................................................2
Capítulo único – Artigo: Use of topographic classes to describe habitat of Amazon forest
birds……………….....................................................................4
Conclusões..............................................................................................................................49
Apêndices.................................................................................................................... ...........50
1
Introdução
Reconhecer o habitat das espécies permite o entendimento dos requisitos necessários para sua
sobrevivência, distribuição, monitoramento e conservação. Aves em geral mostram notável
especialização de habitat de acordo com fitofisionomias (Terborgh et al. 1990, Stotz et al.
1996). Na Amazônia, a grande diversidade de espécies de aves ocorre no que parece ser um
ambiente relativamente homogêneo conhecido como “floresta tropical úmida” (Holdridge
1967), que se estende por milhares de quilômetros quadrados. Na realidade a floresta
amazônica inclui diversas formações vegetais distintas. Mesmo assim, as classificações de
habitat de aves amazônicas se resumem atualmente em fitofisionomias amplas, como “mata
de terra firme”, “várzea”, “campina”, “campinarana” e “capoeira” e, dentro dessas, estratos
como “sub-bosque”, “estrato médio”, “dossel”, “borda de mata” (Terborgh 1985, Terborgh et
al. 1990; Hilty e Brown 1986; Ridgely e Tudor 1994; Stotz et al.1996).
No entanto, a floresta amazônica pode ser mais finamente classificada. Por exemplo,
Veloso et al. (1991) subdivide fitofisionomias brasileiras hierarquicamente em até cinquenta
tipos diferentes. Sendo a floresta amazônica principalmente classificada como “Floresta
Ombrófila Densa”, subdividida em cinco formações de acordo com uma hierarquia
topográfica, que reflete fitofisionomias. No entanto, ao considerar uma mesma fitofisionomia,
ainda podem ser reconhecidos ambientes com características vegetacionais distintas em escala
fina. Por exemplo, índios no Alto Rio Negro identificam seis tipos diferentes de
fitofisionomias dentro do que é tipicamente chamado de campinarana (Abraão et al. 2008).
Na Amazônia, existe diferenciação na avifauna entre pontos e entre locais dentro da mesma
fitofisionomia (Terborgh et al. 1990, Menger 2011). Portanto, se as classificações dentro da
mesma fitofisionomia fossem claramente definidas e mapeadas seria interessante aplicá-las ao
conhecimento da preferência e especialização das aves pelo ambiente.
Nas escalas mais finas dentro de uma mesma fitofisionomia, o habitat para aves é
tipicamente classificado usando estrutura de vegetação (MacArthur et al. 1966, Cody 1981,
Holmes e Robinson 1981, Terborgh 1985), que, por sua vez, está relacionada à mudanças na
composição de comunidades de aves (e g. MacArthur et al. 1966, Cody 1981, Cintra e Naka
2012). De fato, a vegetação é o fator dominante na seleção de habitat pelas aves, embora elas
também selecionem um ambiente de acordo com outras características bióticas e abióticas
(Karr e Freemark 1983). Isso porque a vegetação influencia tanto a disponibilidade de
2
recursos como a estrutura do habitat e condições ambientais, como luminosidade, temperatura
e umidade (Karr e Freemark 1983, Terborgh et al. 1990, Pomara et.al 2012).
A topografia está relacionada à diferenças geoquímicas, drenagem e hidrologia dos
solos, o que influencia a estrutura e composição da vegetação em formações topográficas
como platôs, vertentes e baixios na floresta amazônica (Lucas e Chauvel 1992, Laurance et al.
1999, Castilho et al. 2006, Zuquim et al. 2008), o que permite o reconhecimento de
fitofisionomias referentes às unidades topográficas. Mudanças na topografia foram
relacionadas a variações na comunidade de aves (Bueno 2010, Cintra e Naka 2012), e a
composição de espécies de aves pode ser atribuída à diferenças intrínsecas dos habitats
(Terborgh 1985). Neste contexto, é presumível propor que as formações conhecidas como
florestas de platôs, florestas de vertentes e florestas de baixio (Prance et al. 1976, Hopkins
2005) possam representar habitats distintos para as aves.
Neste estudo procuramos investigar se essas classes topográficas representam habitat
para aves amazônicas, utilizando a abordagem de uso de cada ambiente versus sua
disponibilidade em áreas de floresta primária contínua ao Norte de Manaus. Dentro deste
escopo, pretendemos identificar preferências e especializações de espécies de aves por estes
ambientes e assim associá-las a habitats que representam uma classificação usual mais
específica dentro da floresta de terra firme.
Objetivos
Investigar se classes de relevo topográfico descrevem habitat de aves de terra firme
amazônicas.
Objetivos específicos
1) Testar se existe diferença no uso das classes topográficas platô, vertente, baixio pelas
espécies de aves;
2) identificar preferência das espécies de aves nessas classes ou combinação de duas classes;
3) identificar especialização das espécies de aves nessas classes ou combinação de duas
classes e;
4) testar se espécies associadas a igarapés na literatura mostram preferência ou especialização
a baixios.
3
Capítulo único
________________________________
Rizzi, M.R., Anciães, M., Soto, N.T., Watzel, S.,
Andretti, C.B., Vargas, C., Costa, T., & Cohn-
Haft, M. Use of topographic classes to
describe habitat of Amazon forest birds.
Manuscrito formatado para Oecologia.
4
Author Contributions: MRR developed GIS methodology and analyzed the data. MA collaborated in developing
methodology and analyses. MCH conceived and designed fieldwork. CBA, TVVC, CV performed fieldwork.
MCH, MRR originally formulated the idea and wrote the manuscript; other authors provided editorial advice.
Use of topographic classes to describe habitat of Amazon forest birds 1
2
Maíra Remonatto RIZZI1*
, Marina ANCIÃES2, Nayara Tartari SOTO
1, Stéphany WATZEL
1, 3
Christian Borges ANDRETTI3,4
, Claudeir VARGAS4, Thiago Vernaschi Vieira da COSTA
5, 4
& Mario COHN-HAFT4 5
6
1 Programa de Pós graduação em Ecologia, Instituto Nacional de Pesquisas da Amazônia – 7
INPA, Rua Efigênio Sales, 2239, Aleixo, CEP 69060-020 Manaus- AM, Brazil. 8
2 Coordenação de Biodiversidade, Instituto Nacional de Pesquisas da Amazônia – INPA, Rua 9
Efigênio Sales, 2239, Aleixo, CEP 69060-020 Manaus- AM, Brazil. 10
3 Instituto Pró-Pampa (IPPAMPA), Rua Uruguai, 1242, Centro, CEP 96010-630, Pelotas, RS, 11
Brazil. 12
4 Departamento de Biodiversidade e Coleções Zoológicas - Aves, Instituto Nacional de 13
Pesquisas da Amazônia – INPA, Av. André Araújo, 2936, Petrópolis, CEP 69067-375 14
Manaus, AM, Brazil. 15
5 Museu de Zoologia da Universidade de São Paulo (USP) – Coleções Zoológicas (Aves), Av. 16
Nazaré, 481, Ipiranga, CEP 04263-000, São Paulo, SP, Brazil. 17
* Corresponding author: [email protected] 18
19
ABSTRACT 20
Bottomlands, slopes, and plateaus are classes that harbor distinct characteristics of soil and 21
vegetation, which are important for habitat selection by birds, being potentially recognizable 22
as habitat by Amazon bird species. To test whether species of terra firme forest birds select, 23
5
are specialized in, or avoid any of these environments, we considered the approach of use of 24
topographic classes versus its availability. Available environment was the frequency of each 25
class in 600 ha of primary forest analyzed. The classification was performed using GIS tools 26
and considered the height and percent of slope. Bottomlands were defined as the lowest areas, 27
and slopes and plateaus were defined by the percent of slope above elevation limit for 28
bottomlands. Bird data were obtained in point count censuses in six 100-ha plots, each 29
containing 36 points, visited one to four times per year from 2005 to 2009. Slope was the 30
most abundant class (47%), followed by plateaus (33%) and bottomlands (20%). In 160 31
species analyzed, 50 (31%) selected topographical classes, none were specialized and 16 32
(32%) avoided bottomland or plateau, but never slope. Our results showed that for a few 33
species typically found in forest streams, the bottomland class was the preferred. For other 34
species, these classes associations represented novel hypotheses to be investigated in greater 35
detail. Previous natural history knowledge of these species suggests that the relationship 36
between topographical category and specific habitat requirements are indirect, and the use of 37
these classes to describe habitat should also consider relevant micro-habitat features for birds. 38
KEYWORDS: Neotropical birds, relief, GIS, point-counts, “terra-firme” forest. 39
40
INTRODUCTION 41
Recognizing species habitat allows the understanding of necessary requirements for their 42
survival, distribution, monitoring and conservation. Birds generally show noteworthy 43
specialization of habitat according to vegetation types (Terborgh et al. 1990, Stotz et al. 44
1996). In Amazon, the great diversity of bird species usually occurs in a commonly known 45
relatively homogeneous "rainforest" (Holdridge 1967), extending over thousands of 46
kilometers. However, the Amazon rainforest contains several distinct vegetal formations, and 47
6
the classifications of Amazonian bird habitats are summarized currently in broad vegetation 48
types, such as terra firme, várzea (flood forests), campina, campinarana (white sand forests) 49
and capoeira (second grow forests) and, within these, strata such as "understory", "midstory", 50
"canopy", "forest edge" (Terborgh 1985, Terborgh et al. 1990, Hilty and Brown 1986, Ridgely 51
and Tudor 1994, Stotz et al.1996). 52
But, the Amazon rainforest can be more finely classified since was classified as "Rain 53
Forest" and subdivided into five formations according to topographic hierarchy, which reflect 54
vegetation types (Veloso et al. 1991). In fact, when considering the same vegetation 55
environments distinct features in fine-scale can be recognized. For example, Indians in the 56
Upper Rio Negro identify six different types of vegetation within typically called 57
campinarana (Abraão et al. 2008). In the Amazon, there is no differentiation in composition 58
of bird community between locations within the same vegetation type at local scales 59
(Terborgh et al. 1990, Menger 2011). Therefore, if the classifications within the same 60
vegetation type were clearly defined and mapped, they would be useful to study the 61
preference and specialization of the environment and then refining habitat descriptions for 62
birds. 63
Within the same vegetation type, habitat for birds is typically classified using 64
vegetation structure (MacArthur et al. 1966, Cody 1981, Holmes and Robinson 1981, 65
Terborgh 1985), which in turn is related to changes in the composition of the bird 66
communities (e. g. MacArthur et al. 1966, Cody 1981, Cintra and Naka 2012). As well, the 67
vegetation is the dominant factor in habitat selection by birds, although they also select 68
habitat according to other biotic and abiotic characteristics (Karr and Freemark 1983). Indeed 69
vegetation structure influences the availability of resources, habitat structure and 70
7
environmental conditions such as light, temperature and humidity (Karr and Freemark 1983, 71
Terborgh et al. 1990, Pomara et al. 2012). 72
In the Amazon rainforest, topography is related to geochemical composition, drainage 73
and hydrology differences of soil properties, which influences the structure and composition 74
of vegetation in topographic classes known as plateaus, slopes and bottomlands (Lucas and 75
Chauvel 1992, Laurance et al. 1999, Castilho et al. 2006, Zuquim et al. 2008) enabling the 76
recognition of vegetation types related to these topographic environments. By definition, 77
plateaus are recognized as the higher areas with flat terrain, clayed and well drained soils 78
(Chauvel 1982). Bottomlands comprise lower areas of the terrain, being close to streams, 79
where the soil stays soggy mainly in the rainy season. Slopes are transitional environments, or 80
ecotones, between bottomlands and plateaus, having a high degree of declivity. Slopes soils 81
are composed by clay in the higher portions and by sandy-loamy in the lower portions, 82
varying with the maturity of slopes (Lucas and Chauvel 1992). Usually, transition between 83
bottomland and slopes are more abrupt than between plateaus and slopes (Hopkins 2005, 84
Zuquim et al. 2008). 85
The differences between each topographic class represent distinct vegetation 86
formations. Plateau forests contain the oldest and largest trees. The canopy height varies 87
between 30 and 40 m, with occasional emergent trees reaching 50 or 60 m. In slope forests 88
the plant species composition and tree canopy height are similar to the plateau forests, 89
however there are smaller amount of emergent trees. Finally, bottomland forests occur along 90
streams. Most part of the trees species have shallow roots and the canopy height is relatively 91
lower, ranging from 25 to 30 m, with predominance of palm trees (Prance et al. 1976, Bravard 92
and Righi 1989, Ranzani 1980, Luizão and Vasconcelos 2002, Hopkins 2005). These changes 93
in topography are related to changes in bird community (Bueno 2010, Cintra and Naka 2012), 94
8
and the composition of bird species can be attributed to intrinsic differences of habitats 95
(Terborgh 1985). 96
It is plausible to propose that the formations known as plateaus, slopes and bottomland 97
forests may represent distinct environments that may be differently occupied by birds. In this 98
study, we investigated whether these classes could allow us to describe variation in habitat 99
use by Amazonian birds, based in the use of each environment by bird species versus its 100
availability in continuous primary forest. To quantify availability of each environment we 101
used elevation models derived from Geographic Information System (GIS) tolls. Within this 102
scope, we aimed to identify preferences and specializations of bird species for these 103
environments. We also associated it with previous habitats description in literature in order to 104
verify if our topographic classification could represent a more specific usual classification 105
within the upland forest. 106
107
MATERIAL AND METHODS 108
Studied Area 109
Sampling was conducted in six plots in primary forest studied by the Tropical Ecology 110
Assessment and Monitoring Network (TEAM) project, located at north of Manaus. These 111
plots were located in three research areas of Instituto Nacional de Pesquisas da Amazônia 112
(INPA), being two plots per area. One area is the Reserva Ducke, comprising “Ducke Base” 113
(W59.95, S2.93) and “Ipiranga”(W59.90, S2.97) plots. The other two were located into two 114
dirt roads located in opposite sides of the highway BR 174 (Manaus - Boa Vista): ZF2 (next 115
to the Large Scale Biosphere - Atmosphere Experiment in Amazonia - LBA) and ZF3 (the 116
Biological Dynamics of Fragment Forest - PDBFF), comprising “Km 34 LBA” (W60.21, 117
9
S2.62) and “Km 14 LBA” (W60.11, S2.60), and "Cabo Frio" (W59.90, S2.44) and "Km 37" 118
(W59.79, S2.43) plots, respectively (Figure 1). 119
The region of the sampled areas has annual rainfall varying between 1,800 and 2,500 120
mm, with an average annual temperature of 26 º C. The rainy season is from November to 121
May, with the wettest months in March and April. The dry season is from June to October, 122
and the month of September is usually the driest (Chauvel 1982, Rankin-de-Merona et al. 123
1992, Oliveira et al. 2008). The topography consists of plateaus dissected by streams and the 124
soils are nutrient-poor sandy or clay-rich ferrasols (Chauvel et al. 1987). Vegetation is 125
characterized by terra firme lowland rainforest. The canopy ranges from 30 to 40 m height 126
with emergent reaching 55 m (Chauvel 1982, Guillaumet and Kahn 1982). 127
128
Bird Sampling 129
Data were obtained from bird database of the TEAM Project (Tropical Ecology 130
Assessment & Monitoring Network) collected by a group of experienced ornithologists from 131
Instituto Nacional de Pesquisas da Amazônia, composed by: C. Andretti, C. Vargas, T. Costa, 132
coordinated by M. Cohn-Haft. The samples were taken in six plots of 100 ha (1 km x 1 km), 133
one located in each sampling area described above, thereby totaling 600 ha of primary forest. 134
In each plot there were 36 points separated by 200 m from each other, in a total of 216 points 135
(72 points per area). The plots were sampled during the years 2005-2009, one to four times a 136
year, for a total of 14 sampling campaigns. Data collection of birds followed the method of 137
variable-radius point counts, described in Protocol Birds 3.1 Project TEAM 138
(www.teamnetwork.org/files/protocols/avian/TEAMAvian-PT-EN-3.1.pdf). Each bird 139
recorded was also associated in different position according to its distance from the sampling 140
point (the observer). In this study we considered only registers up to 50 m away from the 141
10
observer, due to an increase of uncertainty to identify the birds associated with longer 142
distances from the observer in this method (Buckland et al. 2001). 143
144
Topographic classes 145
In this study, plateaus, slopes and bottomlands classes were classified through 146
imagery products generated by Digital Elevation Models (DEM) derived from Shuttle Radar 147
Topography Mission (SRTM), data provided by the Instituto Nacional de Pesquisas Espaciais 148
(INPE), in Brazil. Derivation of original SRTM data, whose spatial resolution is 3 arc-seconds 149
(approximately 90 m), includes steps of gap filling, refinement, derivation and post-150
processing, resulting in a digital elevation model (DEM) of height with spatial resolution of 1 151
arc-second (approximately 30 m), which was acquired free of cost from the Geomorphometric 152
Database TOPODATA project of Instituto Nacional de Pesquisas Espaciais (INPE) (more 153
details at: http://www.dsr.inpe.br/topodata/). We used this method, because the data from 154
satellite imagery captures the topographical variation of the land and are widely available. 155
This enabled its use in ecological studies, being consistent with data obtained by traditional 156
methods, but at lower cost (Dent and Yung 1981, Valeriano 2004, Pic et al. 2007, Schietti et 157
al. 2007, Carvalho 2009, Nobre et al. 2011). 158
From these images, the classes were created using two topographic terrain attributes: 159
percent of slope and height. The percent of slope was generated using the slope tool from 160
ArcMap 9.3 software, obtaining a product in percent of inclination. Height data were obtained 161
from the project TOPODATA and processed to obtain the height above the nearest drainage 162
(HAND) (Rennó et al. 2008, Nobre et al. 2011), which normalizes the topography in respect 163
to the drainage network, providing an image with continuous height values in meters. The 164
height above the nearest drainage (HAND) product was generated using a script tool (Figure 165
11
2, more details in Online Resource 1) in the ArcGIS 9.3 software. The height data obtained 166
with height above the nearest drainage (HAND) product were matched to percent of slope 167
data to delineate topographic classes. Then, for each plot, the classes were created according 168
to maximum and minimum thresholds of height and percent of slope (Table 1). 169
Bottomland was defined as the portion of the terrain below a height threshold, which 170
varied between areas. For the portion of the land above this bottomland height threshold, 171
slopes and plateaus were defined using percent of slope. Determination of height and percent 172
of slope thresholds involved visual examination of each area. Slope was preliminarily 173
established as having percent of slope greater than or equal to 7.6% (Nobre et al. 2011). 174
Bottomland was preliminarily established as having height lower than or equal to 8 m (B. 175
Nelson pers. communication). Both values were adjusted for each area in order to classify all 176
image pixels in one of the three topographical classes. This was done taking in account the 177
gradients of height and percent of slope of original digital elevation model (DEM) of each 178
plot. Thus, for bottomland, the final threshold value of height was set from 7.61 to 7.96 m, 179
depending on the analyzed plot. Whereas, for slope, the final threshold value of percent of 180
slope ranged from 7.60 to 7.66% (Table 1). Plateau was subsequently defined as that class 181
with height greater than that found to define bottomland and with percent of slope lower than 182
that found to define slope class. 183
Then, in each of the six plots, the georreferenced sampled points (36 points per plot) 184
were matched to the respectively classified image product to obtain the topographic class 185
associated with those points. After the topographic class was obtained for each point of the six 186
plots, the plots were grouped together, totalizing 216 points and 600 ha of primary forest. 187
188
189
12
Analyses 190
Analysis of habitat selection was performed considering only those species that 191
occurred in more than three points, having at least nine records and, as mentioned above, were 192
found within 50 m of the observer. These parameters were set to decrease the chance of 193
species with limited information being classified as species that select some of the 194
topographic classes. Higher set parameters could eliminate those species that have been 195
poorly sampled and also have been found in some less frequent topographic classes. 196
Each species was analyzed individually using two ways: considering the number of 197
records and considering each point where the species was recorded at least once (presence). 198
The first way may bias the results in favor of classes where the species detection could be 199
easier or where the territory of one individual (or couple) could include few sampled points. 200
On the other hand, if all points of occurrence for a species were considered equally, i. e. 201
regardless the number of records for each point, it could bias in favor of points rarely 202
occupied, mainly if a species is normally concentrated in a few points (as lek species). 203
Thus, to test the selection of topographic classes by species, we used the Forage Ratio 204
test (Krebs 1999), which is indicated to measures of preference for resources such as food and 205
habitat. This analysis can identify the preference for different classes in terms of its use versus 206
its availability. In this study, available environment was considered as the proportion of each 207
of the three topographic classes present in 600 ha of forest sampled. 208
Following the analysis proposed in Krebs (1999), we performed a G test to evaluate if 209
each species used the topographic classes differently. Thus, those species that used the 210
environment as expected by chance (i.e. G test not significant), were considered as species 211
generalists in the use of the environment Species that had not been recorded in a particular 212
environment (class) could not be subjected to the G test (since this test cannot be calculated 213
13
using zeros). However, these cases were included in other analyzis (see next step) to highlight 214
the non-use of a topographic class and therefore the selection of other class species. 215
Then, for each species indicated in the previous step as a species that selects 216
environment, we obtained a selection index for each class, to know which one was selected. 217
The selection index was calculated from the ratio of used topographic class (i. e., the 218
proportion of records or presence for a species found by class) and its availability (i. e., the 219
proportion of a class found among the plots) (more details in Krebs 1999). Species with 220
selection index greater than one indicated preference by the class in question, while indexes 221
equal to or lower than one indicated no preference. 222
In addition, considering a species, when more than one selection index was greater 223
than one among the classes, the difference between them was analyzed by chi-square test. 224
This analysis was performed in pairs of topographic classes (comparison between plateau and 225
bottomland; bottomland and slope, and slope and plateau) in order to evaluate if the species 226
selected only one class (when the chi-square test was significant) or both classes (when the 227
chi-square test was not significant). 228
Finally, those species that showed more than 90% of records in a single class were 229
considered specialists for this class. Kratter (1997) used 95% to consider habitat 230
specialization of birds in Amazon bamboo forests, but we decided for a broader threshold for 231
our first evaluation. On the other hand, species with less than 10% of records in a topographic 232
class were considered as avoiding this class. All data were analyzed using R software version 233
2.13.2 (R Development Core Team 2011). 234
235
236
237
14
RESULTS 238
The six forest plots were successfully classified by topography in three categories: 239
bottomland, slope and plateau (Figure 3 and 4). All 216 sampled points were classified to 240
only one of the three classes, regarding its height and percent of slope (Figure 5). Bottomland 241
was represented by 44 points (20%), slope by 102 (47%) and plateau by 70 (33 %). There was 242
an uneven representation of this three classes (χ2 = 18.85, p = 0.004). So, considering all 600 243
ha of forest sampled together, slopes were predominant, followed by plateaus and 244
bottomlands. 245
Covering all sampled points during the five years, 27,066 records of birds were 246
registered. Considering only those within 50 m far from the observer, we found 16,204 247
records that corresponded to 256 species of birds comprising 51 families. Among the 256 248
species, Lipaugus vociferans was the more frequent with 855 records, followed by 249
Herpsilochmus dorsimaculatus (750 records), which was also the species with the highest 250
occurrence in the sample (present in 75% of the points). Ninety six species were recorded in 251
less than four points or less than nine times, and therefore were left out of the analysis (Online 252
Resource 2). 253
Of the total species, 160 were used in analysis. When considering the number of 254
records, 50 (31%) species showed environment selection (G> 5.9915; p <0.05), with 255
preference for one or two topographic classes (Table 2). Bottomland was selected by 17 256
species (14 families), slope by five (3 families) and plateau by 19 (13 families). Eight species 257
selected some combination of two environments, being seven for slope-plateau and one for 258
bottomland-slope. No species was specialist (> 90% of records in only one class), but 16 259
avoided some class (<10% of records for that environment). Three species avoided plateau 260
15
and 13 avoided bottomland, while slope was never avoided (Table 2). Nine species were 261
absent in some of these classes (Online Resource 2). 262
Species with fewer records tended to occupy less points than the species most 263
recorded (R2 = 0.87, p <0.01), showing an expected pattern of the data. Also, considering only 264
the presence/absence data (not counting multiple records in the same point and removing their 265
effect in the preference analysis), a total of ten species selected topographic classes (Table 2, 266
species highlighted in bold). Nine of them were also recognized in the previous analysis 267
(number of records) and one species selected two classes only in presence analyses. 268
Considering this particular case, a total of 51 species selected topographic classes. 269
Although the definition of selection involved the use of environments versus its 270
availability, for some species the selected environment (topographic class) was not that in 271
which the species was more recorded (species underlined in Table 2). In all these cases, slope 272
(the more abundant class present in the studied areas) accumulated more records than 273
bottomland and plateau. 274
275
DISCUSSION 276
We investigated the preference and specialization of forest bird species by topographical 277
classes, which cover distinct features of soil and vegetation, and are able to reflect the 278
preference of some species. Even using a widely “specialization” definition (see Methods), no 279
species was specialized to a particular class. This is consistent with the lack of the use of these 280
topographical environments to describe bird’s habitats found in most of the ornithological 281
literature (Hilty and Brown 1986, Stotz et al. 1996, Hilty 2002, Schulenberg et al. 2007). 282
However, about 30% of the species showed some preference, reinforcing the notion that these 283
classes of topography exert some relevance to birds. Slope was never avoided, and in some 284
16
cases was used more than any other environment, even when not selected by species. This 285
certainly reflects the prevalence of slopes, as defined herein, in the studied areas, and also 286
indicated that what was classified as part of slopes included used area for many species that 287
actually preferred bottomland or plateau. 288
Considering the lack of clear and technical definition of specialization, the 289
"preference" for topographic classes on species listed in Table 2 should be considered as cases 290
of selection to be further investigated. In each of these cases, described in more details below, 291
some aspects of the natural history of the species described so far reinforce or make consistent 292
this classification. For the other cases, the association appears to be unprecedented and 293
sometimes difficult to explain. As such, we treated these species into groups according to 294
their preference. We interpreted as being “strong cases” those species that showed selection 295
when analyzed their records and when evaluated only their presence (in bold in Table 2), as 296
well as when avoided some environment (with superscript in Table 2). The “weaker cases” 297
were those in which the species were more recorded in slopes than in the "selected" 298
environment (underlined in Table 2). In any group no relationship was observed between 299
species considering the taxonomic family, forest strata used, or sociability (see Cohn-Haft et 300
al. 1997). 301
302
Bottomland species 303
Of the 17 species that selected bottomlands, four are mentioned in the literature as 304
being of wetlands or found near streams within the upland forest. Schistocichla leucostigma is 305
unanimously considered as associated to streams (Hilty and Brown 1986, Cohn-Haft et al. 306
1997, Hilty 2002, Schulenberg et al. 2007, Johnson et al. 2011, Cintra and Naka 2012). 307
Philydor pyrrhodes does not approach the water, getting in subcanopy treetops, but in this 308
17
region of the Amazon this species occurs almost exclusively areas with relatively high density 309
of Euterpe spp. and other typical bottomland palms (Hilty and Brown 1986, Cohn-Haft et al. 310
1997, Hilty 2002). Formicarius analis and Lophotriccus vitiosus are also taken as common 311
near streams and wetland regions within the upland forest (Hilty and Brown 1986, Hilty 2002, 312
Schulenberg et al. 2007, Johnson et al. 2011). Thus, our results were consistent with previous 313
knowledge, which, in fact, show that our classification method is reliable. Other species 314
previous found in the studied area (Cohn-Haft et al. 1997) associated to bottomland or 315
streams (e. g. Kingfishers, Myrmeciza atrothorax, Slateria naevia), were rare on terra firme 316
forest and were not registered during our sampling. Phaeothlypis mesoleuca, species with 317
strong association with streams, was registered on the sampling, but did not range minimum 318
number of records within 50 m far from observer, and therefore was not analyzed. 319
The other species we found associated with bottomlands (Table 2) do not appear as 320
such in the literature, and the characterization in this study represents a novel hypothesis of 321
habitat selection. For example, Lophotriccus vitiosus in previous experience was found in 322
forest edge and partially opened canopy (Cohn-Haft 1995, Cohn-Haft et al. 1997), and even 323
being described as occurring next to streams (Johnson et al. 2011), may also occurs regularly 324
in other elevations. 325
Several species such as Ara ararauna, Touit purpuratus, Mionectes macconnelli, 326
Dixiphia pipra, Coereba flaveola, Lanio surinamus and Euphonia cayennensis are 327
predominantly frugivorous. Their preference may be related with a more opening canopy 328
associated with bottomlands and a greater light penetration may favor higher fruit production 329
in the understory. But for the canopy species such as parrots, for example, is difficult to 330
imagine what would be selecting bottomland throughout the year. We suspected that a 331
temporary abundance and seasonal fruit during our sampling (such as “buriti”, Mauritia 332
18
flexuosa; “acai”, Euterpe spp.; or “patauá”, Oenocarpus bataua) may have concentrated the 333
use of bottomlands along its availability in the environment, but in other times, other 334
topographic classes are also important. 335
For those species that have never been remarked in the literature as being of 336
bottomland, the most plausible explanation for the preference seems to be observed through 337
some specific important environmental feature in the natural history of the species. Thus, 338
bottomland itself is not the causal factor of presence of the species, but these important 339
features for the species may be indirectly linked to this class. For example, the great cotingid 340
Capuchinbird (Perissocephalus tricolor) form leks and is detected almost exclusively by high 341
nuptial vocalizations produced in those leks. We noticed a trend in the field that leks are 342
located on the top of steep slopes, near plateaus, and we believe that this species may be using 343
bottomlands as "acoustic chambers" to propagate its singing (personal observation). In fact, 344
for P. tricolor, bottomland was not the selected environment for leks according to our 345
presence analysis. Also the relative rare proportion of bottomlands in our study may explain 346
why this species seems to prefer (using more than expected) this topographic class, although 347
its leks are not commonly located in this environment. 348
349
Slope species 350
Slope was the most abundant topographic class in our sampled areas. The five species 351
that preferred this environment had their forage ratio test more conservative, since, for our 352
analysis considering the use of topographic classes versus its availability, the number of 353
species records required in this class needed to be much higher than in other environments. 354
Indeed, none of them had been described as having such preference so far. Like for 355
19
bottomland species cases, it seems to be other characteristic, perhaps associated with slopes, 356
which may explain this preference. 357
Cercomacra cinerascens occurs in tangles of vines in the midstory and subcanopy 358
(Hilty 2002) and is notably absent in forests with few vines (personal observation). Since 359
slopes are related with an increase of tree mortality (Toledo et al. 2011, 2012), this may favor 360
the lateral exposure of trees to the sunlight and in turn should encourage the growth of vines 361
in the canopy. Because of this, slopes may offer a more suitable environment for this species. 362
Related to this fact, other species listed (Table 2) – such as parrots and the toucan - nest in 363
hollow trees, whose presence may also be favored by higher rate of tree mortality in slope. 364
However, all this relationships are tenuous and poorly documented, and does not explain why 365
other sampled species that also nest in cavities or hollow logs did not select slope. 366
367
Plateau species 368
Species that selected plateau occupied various forest strata, and included insectivorous 369
and frugivorous. Also they varied from territorial to accompanying mixed or monospecific 370
flocks species. Besides the fact that our results for Hylopezus macularius is in accordance 371
with what was found by Stratford and Stouffer (2013), where this species was only observed 372
in upland forests in a similar forest area, most species that selected plateau are not directly 373
related to this environment in the literature. Nevertheless, these species have in common the 374
fact that all of them could be called as predominant in extensive primary and well preserved 375
forest. This suggests that plateau forest represents the more stable environment of terra firme, 376
whereas the other two topographic classes have features in common with other forest types 377
(such as campinarana and capoeira). Specifically, it reinforces the idea that bottomland and 378
20
slope suffer higher mortality and falling trees and thus higher light penetration (Toledo et al. 379
2011, 2012), making plateaus more constant environments compared to them. 380
Few of the plateau species found in our study have known preference or tolerance for 381
natural forest gaps or forest regeneration. For example, Bucco tamatia, Thamnophilus 382
murinus, Pipra erythrocephala tolerate gaps and forest edge (Cohn-Haft et al. 1997), and Ara 383
macao seems to be more common in lowland forest called várzea than in the terra firme 384
(pers. obs.). Other species may occupy forest gaps but probably need a primary forest matrix 385
nearby (Hilty and Brown 1986, Cohn-Haft et al. 1997, Hilty 2002, pers. obs.). 386
387
Species that selected two environments 388
All species, but one (see below) that selected two environments preferred the 389
combination of bottomland and slope or plateau and slope. This result suggests that the 390
classes themselves are not good representatives of specific environment for these species. In 391
fact, the terrain zone that encompasses bottomlands and lower part of slopes has a distinct 392
plant species composition compared to upland zones (Schietti et.al 2013), which may contrast 393
the way species perceive their environment. Thus, for such species, the combination of 394
classes may include common characteristics that make them usable as one unique 395
environment. 396
Curiously, Philydor erythrocercum was the only species to prefer bottomland-plateau 397
combination, considering presences. This species occurs in mixed flocks and this fact may be 398
related to the daily movement between these two environments (K. Mokross, unpublished 399
data.). Although this does not explain why this species did not selected this class combination 400
when considering its records and why other species of mixed flocks did not yield similar 401
result. The difference between these extremes classes (bottomland and plateau) regarding 402
21
their height (or other features) may not express a clear limit respected by P. erythrocercum, 403
although this relation is speculative. 404
405
Concluding remarks 406
Although we attempted to explain the results based on observed characteristics of each 407
species, in general, classification of species by topographic classes must be interpreted as a 408
hypothesis to be tested in more detail. The stronger results are that no bird species showed 409
specialization to topographic class. And most cases of selection may be explained as some 410
specific microhabitat preference indirectly associated with topography classes. As 411
demonstrated in Pomara et al. (2012), bird community varies with soil nutrients composition 412
through vegetation structure. Even topographic classes being recognized with different 413
vegetation structure, most birds did not select them as distinct environments. 414
Our topographic classification was based in the topographic classification used by 415
Rennó et al. (2008) and Nobre et al. (2011), which include field validation and the scale used 416
was higher. Our study considered a local scale, where even pixels of 30 x 30 m may have 417
missed terrain features important to birds within the finest plots of 100 ha, as for example a 418
narrow bottomland. Despite this, our classification of pixels was in accordance with the 419
values of height and percent of slope expected for each class (Rennó et al. 2008, Nobre et al. 420
2011). However, using continuous data of height and percent of slope (Figure 3 and 4) such as 421
we did to classified topography was useful to find out some species association with 422
topographic classes. Species associated with wetland, valleys and streams in the literature 423
were in fact identified selecting bottomland using this methodology. 424
Further studies should include attempts to clarify the relationship between topography 425
classes and occupation by birds. Studies of habitat`s use considering the territory of each 426
22
species may allow a better understand of which environments are more or less used by 427
species. Another approach could be map the species in topographical relief. This would 428
eliminate the need to congruence of thresholds classes across species, allowing that each 429
species has a topography single use, and may facilitate the identification of other factors 430
relevant for such species. 431
Initially, all birds record position was mapped in landscape taking in account its 432
distance and direction from the observer (raw data). After this, we selected only those birds 433
position that fall within 50 m far from observer. But raw record data can allow us to verify an 434
association more robust between birds and their environment, mainly considering its territory 435
and living area as well. To do it so, we are developing a software that will be able to precisely 436
georreference all the birds record position, allowing us to do a more accurate analysis in other 437
studies. 438
Finally, our work represented an initial attempt to quantify the use of environment 439
versus its availability to find out a more refined and functional habitat classification for birds 440
within the terra firme forest. Thus, we conclude that topographic classes themselves do not 441
represent a subdivision of the forest with strong explanatory power of the local distribution of 442
bird species, and does not represent habitat for Amazonian terra firme forest birds. However, 443
the altitudinal gradient within the forest is associated with suitable characteristics for many 444
species. This knowledge allows to identify birds specific environments associations and to 445
understand how these animals are using upland forests in Amazonia. Our results show that 446
although habitat classification is useful to understand habitat`s use by some bird species, 447
specific environmental components, related or not to topographic classes may be more 448
important. Therefore, monitoring efforts of Amazonian birds should also consider specific 449
23
preferences for the recognition of important areas for conservation of these species and not 450
only consider those broad Amazon forest classifications. 451
452
ACKNOWLEDGMENTS 453
We thank National Council for Scientific and Technological Development (CNPq) for MSc 454
scholarship to MRR and Tropical Ecology Assessment & Monitoring Network Project 455
(TEAM) for providing data and financial support. We also thank to Dr. Bruce Nelson for his 456
relevant help in the generation of elevation models of the studied areas, and to Dr. Victor 457
Lemes Landeiro for his contributions in script automation in R software. 458
459
REFERENCES 460
Abraão MB, Nelson BW, Baniwa, JC, Yu DW, Shepard Jr GH (2008) Ethnobotanical ground 461
– truthing: indigenous knowledge, floristic inventories and satellite imagery in the upper Rio 462
Negro, Brazil. Journal of Biogeography, 35:2237-2248 463
464
Bravard S, Righi D (1989) Geochemical differences in an Oxisol-Spodosol Toposequence of 465
Amazônia, Brazil. Geoderma 44(1):29-42 466
467
Buckland ST, Anderson DR, Burnham KP, Laake JL, Borchers DL, Thomas L (2001) 468
Introduction to distance sampling: Estimating abundance of biological populations. Oxford 469
University Press, New York, USA. 470
471
Bueno AS (2010) Distribuição de aves de sub-bosque ao longo de gradientes ambientais na 472
Amazônia Central. Master thesis, Department of Ecology, Instituto Nacional de Pesquisas da 473
Amazônia, Manaus, Amazonas, Brazil 474
24
475
Carvalho TM (2009) Parâmetros geomorfométricos para descrição do relevo da Reserva de 476
Desenvolvimento Sustentável do Tupé, Manaus, Amazonas. In: Santos-Silva EM, Scudeller 477
VV (eds) Biotupé: Meio Físico, Diversidade Biológica e Sociocultural do Baixo Rio Negro, 478
Amazônia Central. Universidade Estadual do Amazonas, Manaus, pp 3-17 479
480
Castilho CV, Magnusson WE, Araújo RNO, Luizão RC, Luizão FJ, Lima AP, Higuchi N 481
(2006) Variation in aboveground tree live biomass in a central Amazonian forest: effects of 482
soil and topography. Forest Ecology and Management, 234:85-96 483
484
Chauvel A (1982) Os latossolos amarelos, alico, argilosos dentro dos ecossistemas das bacias 485
experimentais do INPA e da região vizinha. Suplement Acta Amazônica, 12(3):47-60 486
487
Chauvel A (1987) On the genesis of the soil mantle of the region of Manaus, Central 488
Amazonia, Brazil. Experientia 43:234-241 489
490
Cintra R, Naka L (2012) Spatial variation in bird community composition in relation to 491
topographic gradient and forest heterogeneity in a Central Amazonian rainforest. International 492
Journal of Ecology doi:10.1155/2012/ 435671 493
494
Cody M (1981) Habitat selection in birds: The roles of vegetation structure, competitors, and 495
productivity. Bioscience 31(2):107-113 496
497
Cody M. L (1985) Habitat selection in birds. Academic Press, New York, NY,USA 498
25
499
Cohn-Haft M (1995) Evolution of avian dietary specialization along an environmental gradient: 500
Tropical rainforest interior versus canopy and edge habitats. Master thesis, Department of 501
Ecology, Evolution, and Organismal biology, Tulane University, New Orleans, Los Angeles, 502
USA. 503
504
Cohn-Haft M A, Whittaker A, Stouffer PC (1997) A New Look at the "Species-Poor" 505
Central Amazon: The Avifauna North of Manaus, Brazil. Ornithological 506
Monographs 48: 205-235 507
508
Comitê Brasileiro de Registros Ornitológicos (2011) Listas das aves do Brasil, 10ª Edição. 509
(http://www.cbro.org.br). Access in: 02/03/2012 510
511
Dent D, Young A (1981) Soil survey and land evaluation, George Allen and Unwin (Eds), 512
London, UK 513
514
Guillaumet J, Kahn F (1982) Estrutura e dinamismo da floresta. Acta Amazônica 12: 61-77. 515
516
Hilty SU, Brown WL (1986) A guide to the birds of Colombia. Princeton University Press, 517
Princeton, NJ, USA 518
519
Hilty SU (2002) Birds of Venezuela, 2nd edn. Princeton University Press, Princeton, NJ, USA 520
521
Holdridge LR (1967) Life zone ecology. Tropical Science Center, San Jose, Costa Rica 522
523
26
Holmes RT, Robinson SK (1981) Tree species preferences of foraging insectivorous birds in 524
a Northern Hardwoods Forest. Oecologia 48: 31-35 525
526
Hopkins MJG (2005) Flora da Reserva Ducke, Amazonas, Brazil. Rodriguésia 56 (86):9-25 527
528
Johnson EI, Stouffer PC, Vargas FC (2011) Diversity, biomass, and trophic structure of a 529
central Amazonian rainforest bird community. Revista Brasileira de Ornitologia 19(1):1-16 530
531
Karr JR, Fremark KE (1983) Habitat selection and environmental gradients: Dynamics in the 532
“stable” tropics. Ecology 64(6):1481-1494 533
534
Kirwann G (2012) Cotingas and manakins. Pinceton University Press, Princeton, NJ, USA 535
536
Kratter AW (1997) Bamboo specialization by Amazonian birds. Biotropica 29 (1):100-110 537
538
Krebs CJ (1999) Ecological Methodology, 2nd edn. Addison Wesley Longman, USA 539
540
Laurance WF, Fearnside PM, Laurance SG, Delamonica P, Lovejoy TE, Rankin-de Merona 541
JM, Chambers JQ, Gascon C (1999) Relationship between soils and Amazon forest biomass: 542
a landscape-scale study. Forest Ecology and Management 118: 127-138 543
544
Lucas Y, Chauvel A (1992) Soil formation in tropically weathered terrains. In: Butt CRM, 545
Zeegers H (eds) Regolith exploration geochemistry in tropical and subtropical terrains. 546
Elsevier Science Publishers B.V., Amsterdam, Netherlands 547
27
548
Luizão F.J, Vasconcelos HL (2002) Floresta Tropical Úmida (Manaus): Site 1. In: Seeliger U, 549
Cordazzo C, Barbosa F (eds) Os sites e o programa brasileiro de pesquisas ecológicas de 550
longa duração. Universidade Federal de Minas Gerais, Belo Horizonte, Minas Gerais, Brazil 551
552
MacArthur R, Recher H, Cody M (1966) On the relation between habitat selection and 553
species diversity. The American Naturalist 100: 319-332 554
555
Menger JS (2011) Fatores determinantes da distribuição de aves no interflúvio Purus-556
Madeira. Master thesis, Department of Ecology, Instituto Nacional de Pesquisas da 557
Amazônia, Manaus, Amazonas, Brazil. 558
559
Nobre AD, Cuartas LA, Hodnett M, Rennó CD, Rodrigues G, Silveira A, Waterloo M, 560
Saleska S (2011) Height above the nearest drainage – a hydrologycally relevant new terrain 561
model. Journal of Hydrology 404:13-29 562
563
Oliveira ML, Baccaro FB, Braga-Neto R, Magnusson WE (2008) Reserva Ducke: A 564
biodiversidade amazônica através de uma grade. Áttema Desing Editorial, Manaus, Amazonas, 565
Brazil 566
567
Pic A, Rennó CD, Pinheiro TF, Soares JS (2007) Avaliação da influência da vegetação nos 568
dados SRTM para a região amazônica. Anais XIII Simpósio Brasileiro de Sensoriamento 569
Remoto, Florianópolis, Santa Catarina, Brazil 570
571
28
Pomara LY, Ruokolainen K, Tuomisto H, Young KR (2012) Avian composition co-variates 572
with floristic composition and soil nutrient concentration in Amazonian upland forests. 573
Biotropica 0(0): 1-9 doi: 10.1111/j.1744-7429.2011.00851.x 574
575
Prance GT, Rodrigues WA, Silva MF (1976) Inventário florestal de um hectare de mata de 576
terra firme km 30 da Estrada Manaus-Itacoatiara. Acta Amazônica 6(1): 9-35 577
578
R Development Core Team (2011) R: A language and environment for statistical computing. 579
R Foundation for Statistical Computing, Vienna, Austria. ISBN 3-900051-07-0, URL 580
http://www.R-project.org/ 581
582
Rankin-de-Merona JM, Prance GT, Hutchings RW, Silva FM, Rodrigues WA, Uehling ME 583
(1992) Preliminary results of large-scale tree inventory of upland rain forest in the central 584
Amazon. Acta Amazonica 22:493-534 585
586
Ranzani G (1980) Identificação e caracterização de alguns solos da Estação Experimental de 587
Silvicultura Tropical do INPA. Acta Amazônica 10(1):7-41 588
589
Rennó CD, Nobre AD, Cuartas LA, Soares JV, Hodnett MG, Tomasella J, Waterloo M (2008) 590
HAND, a new terrain descriptor using SRTM-DEM: Mapping terra-firme rainforest 591
environments in Amazonia. Remote Sensing of Environment, 112:3469–3481 592
593
Ridgely RS, Tudor G (1994) The birds of South America, vol. I. The Oscine Passerines. 594
University of Texas Press, Austin, Texas, USA 595
29
596
Schietti J, Drucker D, Keizer E, Carneiro-Filho A, Magnusson W (2007) Avaliação do uso de 597
dados SRTM para estudos ecológicos na Amazônia central. Anais XIII Simpósio Brasileiro 598
de Sensoriamento Remoto, Florianópolis, Santa Catarina, Brazil 599
600
Schietti J, Emilio T, Rennó CD, Drucker DP, Costa FRC (2013) Vertical distance from 601
drainage drives floristic composition changes in an Amazon rainforest. Plant Ecology and 602
Diversity, doi 10.1080/17550874.2013.783642 603
604
Schulenberg TS, Stotz DF, Lane DF, O’Neill JP, Parker III PT, Egg AB (2007) Birds of Peru. 605
Princeton University Press, Princeton, NJ, USA 606
607
Strattford JA, Stouffer PC (2013) Microhabitat associations of terrestrial insectivorous birds 608
in Amazonian rainforest and second-growth forests. Journal of Field Ornithology, 84(1):1-12 609
610
Stotz D, Fitzpatrick JW, Parker III TA, Moskovits K (1996) Neotropical Birds, Ecology and 611
Conservation. The University of Chicago Press, Chicago, Illinois, USA 612
613
Terborgh J (1985) Habitat selection in Amazonian birds In: Cody M. L (eds) Habitat 614
Selection in Birds. Academic Press, New York, NY, USA 615
616
Terborgh J, Robinson SK, Parker III TA, Munn CA, Pierpont N (1990) Structure and 617
organization of an Amazonian forest bird community. Ecological Monographs 60(2):213-238 618
619
30
Toledo JJ, Magnusson WE, Castilho CV, Nascimento HEM (2011) How much variation in 620
three mortality is predicted by soil and topography in Central Amazonia?. Forest Ecology and 621
Management 262:331-338 doi:10.1016/j.foreco.2011.03.039 622
623
Toledo JJ, Magnusson WE, Castilho CV, Nascimento HEM (2012) Tree mode of death in 624
Central Amazonia: Effects of soil and topography on tree mortality associated with storm 625
disturbances. Forest Ecology and Management 263:253-261 doi:10.1016/j.foreco.2011.09.017 626
627
Valeriano MM (2004) Modelo digital de elevação com dados SRTM disponíveis para a 628
América do Sul. Instituto Nacional de Pesquisas Espaciais, São José dos Campos, São Paulo, 629
Brazil 630
631
Veloso HP, Rangel-Filho ALR, Lima JCA (1991) Classificação da Vegetação Brasileira 632
Adaptada a um Sistema Universal. Instituto Brasileiro de Geografia e Estatística, Rio de 633
Janeiro, RJ, Brazil 634
635
Zuquim G, Costa FRC, Prado J, Tuomisto H (2008) Guia de samambaias e licófitas da 636
REBIO Uatumã – Amazônia Central. Áttema, Manaus, Amazonas, Brazil 637
31
Table 1. Height and percent of slope thresholds used to classify each
plot studied. Height represents the maximum value in meters used to
defined bottomland. Above this height threshold, using the percent
of slope, slopes and plateaus were, respectively, defined as having
higher and lower values than that showed in the table (see Figure 2).
Plot Name Percent of slope (%) Height (m)
1 Ducke Base 7.66 7.95
2 Ipiranga 7.60 7.91
3 Cabo Frio 7.60 7.69
4 Km 37 7.60 7.69
5 34 LBA 7.64 7.96
6 14 LBA 7.60 7.71
32
Table 2. Species that selected topographic classes. Species were grouped according to the
selected environment. Species in bold refer to those also classified considering presences. The
superscript letters indicate those species that avoided environment (less than 10% of records),
where P means plateau and B means bottomland. Underlined species were more recorded in
slope than in the selected class (see the values in Online Resource 2). Species name is in
accordance with Comitê Brasileiro de Registros Ornitológicos (2011).
BOTTOMLAND
Odontophorus gujanensis P Pithys albifrons Lophotriccus vitiosus
Ara ararauna Formicarius analis Myiopagis gaimardii
Touit purpuratus Philydor pyrrhodes
Coereba flaveola
Phaethornis bourcieri Dixiphia pipra Lanio surinamus
Phaethornis superciliosus Perissocephalus tricolor Euphonia cayennensis
Schistocichla leucostigma P Mionectes macconnelli
SLOPE
Amazona farinosa Deroptyus accipitrinus B Pteroglossus viridisB
Amazona autumnalis Cercomacra cinerascens
PLATEAU
Ara macao Hylopezus macularius B Hemitriccus zosterops
Chaetura spinicaudus B Lepidocolaptes albolineatus B Vireolanius leucotis*
Bucco tamatia B Dendrocolaptes certhia B Hylophilus ochraceiceps
Celeus torquatus B Pipra erythrocephala Microbates collaris
Myrmeciza ferruginea Pachyramphus marginatus Lanio fulvus B
Thamnophilus murinus Pachyramphus surinamus B Tangara chilensis B
Frederickena viridis
TWO TOPOGRAPHIC CLASSES
SLOPE-PLATEAU BOTTOMLAND-SLOPE BOTTOMLAND-PLATEAU
Celeus undatus Corythopis torquatus P Philydor erythrocercum**
Galbula dea
Geotrygon montana B
Hylophilus muscicapinus
Ibycter americanus B
Melanerpes cruentatus B Selenidera piperivora B
* Selected slope-plateau when considered presence
** Selected bottomland-plateau only when considered presence
33
Figure 1. Localization of the six sampled plots (in white) belonging to the three research areas
of Instituto Nacional de Pesquisas da Amazônia (INPA) (yellow lines), where: a) Reserva
Florestal Adolpho Ducke (a1 - Ducke Base and a2 - Ipiranga), b) ZF 2 (b1 - Km 34 LBA and b2
- Km 14 LBA) and c) ZF 3 (c1 - Cabo Frio and c2 - Km 37.
34
Figure 2. Flowchart of steps to obtain the product HAND (Height Above the Nearest
Drainage). The rectangles symbolize images and parallelograms are tools of ArcGIS 9.3
software. The rectangle with curved edges is a vector data, and the arrows are connectors that
represent the processing order of each item to get the image HAND in meters (more details in
Online Resource 2).
35
Figure 3. Areas that comprise each sampled plot classified in bottomland, slope and plateau
classes. Note the orientation of each plot, each one composed by 36 sampled points. (Figure
color in the electronic version).
36
Figure 4. Thresholds of height and percent of slope of all pixels classified in each area. Each pixel of figure 3
corresponds to a point in the chart area. Bottomlands were defined by height threshold and plateaus and slopes by their
respective percent of slope threshold (Table 1 for more details). Points to the left of the blue line were classified as
bottomland and points above and below the red line were classified as slope and plateau, respectively.
37
Figure 5. All 216 sampled points classified according to height and percent of slope
thresholds established for each plot. In blue, points classified as bottomland (b); in
black as slope (v), and in red as plateau (p).
38
Electronic Supplementary Matterial
39
ESM 1. Tools used in the preparation of height above drainage (HAND) model, based on
processing method developed by Dr. Bruce Nelson (INPA). Settings used are in brackets.
#Definine projection of topodata image
DefineProjection_management
C:\ORIGEM\IMAGEM_TOPODATA.tif (em GCS_WGS84)
#Redefining topodata image projection to UTM (m)projection
Redefinindo a projeção da imagem topodata para UTM em metros
ProjectRaster_management
C:\ORIGEM\IMAGEM_TOPODATA.tif C:\ORIGEM\MDE_UTM_20S.tif
(escolher fuso da imagem topodata)
#Change the data frame projection to UTM (m) projection
#Extract area of interest
ExtractByRectangle_sa
C:\ORIGEM\MDE_UTM_20S.tif C:\ORIGEM\MDE_AREA1.tif (INSIDE)
# In ENVIRONMENTS settings define MDE_AREA.tif as SNAP RASTER
#Fill puddles
Fill_sa
C:\ORIGEM\MDE_AREA1.tif C:\ORIGEM\MDE_AREA1_PREEN.tif
#Calculate flow direction and flow accumulation (of the drainage)
FlowDirection_sa
C:\ORIGEM\MDE_AREA1_PREEN.tif C:\ORIGEM\MDE_AREA1_DIR_ESC.tif
(NORMAL)
FlowAccumulation_sa
C:\ORIGEM\MDE_AREA1_DIR_ESC.tif C:\ORIGEM\MDE_AREA1_ESC_ACUM.tif
(INTEGER)
# Aply expansion contrast min=0 and max=30 to view streams
#Boolean image to identify the streams talvegs
SetNull_sa
C:\ORIGEM\MDE_AREA1_ESC_ACUM.tif C:\ORIGEM\MDE_AREA1_
BOOL_TALVEG.tif (VALUE <270)
#Get the height above the sea level of the talvegs
SingleOutputMapAlgebra_sa
C:\ORIGEM\MDE_AREA1_ BOOL_TALVEG.tif * C:\ORIGEM\MDE_AREA1.tif
C:\ORIGEM\ MDE_AREA1_ALT_TALVEG.tif
#Vector image derived from the height of the talvegs (previous step)
to use to estimating the height of the groundwater
RasterToPoint_conversion
C:\ORIGEM\ MDE_AREA1_ALT_TALVEG.tif C:\ORIGEM\
AREA1_ALT_TALVEG.shp (VALUE)
#Estimate the height of the groundwater by interpolation (adjusting
the pixel size)
40
Idw_sa
C:\ORIGEM\ AREA1_ALT_TALVEG.shp (GRID_CODE) C:\ ORIGEM\ AREA1
_ALT_LENCOL.tif (CellSize 30.8741613, 2, VARIABLE 12)
#Subtract the height of the groundwater from the height above the
sea level (product of second step) to obtain the product height
above drainage (HAND)
SingleOutputMapAlgebra_sa
'C:\ORIGEM\MDE_AREA1.tif - C:\ ORIGEM\ AREA1 _ALT_LENCOL.tif
C:\ORIGEM\ AREA1_ASD.tif
41
ESM 2. Data from the 160 analyzed species. Species that selected environments are marked in gray. The parameters are: number of total records
(regt); proportion of records in each class (reg); proportion of presences in 216 sampling points (prest); proportion of presences in each
environment (pres), test value G (G> 5.99, p<0.05, df. = 2, indicates that the species selected environment; species marked with the symbol *
selected environment with G> 9.21, p<0.01); selection indexes for each environment (w) and values of the chi-square test between two selection
indices (X2> 3.84, df. = 1 indicates significant difference between the indexes tested), where “b” means bottomland, “v” slope and “p” means
plateau. Species name is in accordance with Comitê Brasileiro de Registros Ornitológicos (2011).
Espécie regt regb (%)
regv (%)
regp (%)
prest (%)
presb (%)
presv (%)
presp (%)
G wb wv wp X2bv X2
bp X2vp
TINAMIDAE
Tinamus major 14 28.6 35.7 35.7 6.0 30.8 38.5 30.8 0.89 1.40 0.76 1.10 1.53 0.32 1.08
Crypturellus variegatus 102 12.7 47.1 40.2 21.8 21.3 36.2 42.6 5.14 0.63 1.00 1.24 5.50 11.28 6.49
CRACIDAE
Penelope marail 17 17.6 41.2 41.2 4.6 20.0 30.0 50.0 0.57 0.87 0.87 1.27 0.00 0.77 2.30
ODONTOPHORIDAE
Odontophorus gujanensis 21 61.9 38.1 0.0 2.8 50.0 50.0 0.0 NA 3.04 0.81 0.00 56.94 34.13 12.92
FALCONIDAE
Ibycter americanus 33 0.0 57.6 42.4 8.8 0.0 57.9 42.1 NA 0.00 1.22 1.31 44.79 24.32 1.16
Micrastur ruficollis 17 5.9 35.3 58.8 5.1 9.1 36.4 54.5 5.95 0.29 0.75 1.82 1.86 15.28 31.40
Micrastur gilvicollis 26 26.9 34.6 38.5 6.0 38.5 38.5 23.1 1.74 1.32 0.73 1.19 2.36 0.12 3.50
PSOPHIDAE
Psophia crepitans 91 16.5 53.8 29.7 9.7 14.3 38.1 47.6 1.75 0.81 1.14 0.92 3.86 0.27 4.53
COLUMBIDAE
Patagioenas plumbea 206 17.0 54.4 28.6 17.6 21.1 52.6 26.3 4.29 0.83 1.15 0.88 8.00 0.13 14.41
Patagioenas subvinacea 27 14.8 51.9 33.3 6.9 20.0 46.7 33.3 0.58 0.73 1.10 1.03 1.45 0.67 0.13
Geotrygon montana 21 0.0 57.1 42.9 5.6 0.0 58.3 41.7 NA 0.00 1.21 1.32 28.00 15.75 1.16
PSITTACIDAE
Ara ararauna * 18 50.0 16.7 33.3 5.1 36.4 18.2 45.5 10.25 2.45 0.35 1.03 16.18 11.84 4.08
Ara macao 19 0.0 42.1 57.9 6.0 0.0 53.8 46.2 NA 0.00 0.89 1.79 13.82 26.13 66.60
42
Espécie regt regb (%)
regv (%)
regp (%)
prest (%)
presb (%)
presv (%)
presp (%)
G wb wv wp X2bv X2
bp X2vp
Ara chloropterus 12 8.3 25.0 66.7 4.2 11.1 33.3 55.6 5.94 0.41 0.53 2.06 0.08 14.33 35.97
Brotogeris chrysoptera 224 18.3 53.1 28.6 48.1 22.1 46.2 31.7 3.13 0.90 1.13 0.88 4.26 0.02 12.33
Touit huetii 16 18.8 56.3 25.0 4.6 20.0 50.0 30.0 0.58 0.92 1.19 0.77 0.45 0.09 2.68
Touit purpuratus * 39 43.6 43.6 12.8 12.5 40.7 40.7 18.5 13.87 2.14 0.92 0.40 18.76 22.40 7.55
Pyrilia caica 171 18.7 55.0 26.3 40.3 19.5 51.7 28.7 4.39 0.92 1.16 0.81 3.91 0.48 19.89
Pionus menstruus 237 17.7 52.3 30.0 43.5 18.1 48.9 33.0 2.56 0.87 1.11 0.92 4.96 0.18 7.48
Pionus fuscus 120 24.2 46.7 29.2 31.9 24.6 46.4 29.0 1.21 1.19 0.99 0.90 1.48 2.32 0.72
Amazona farinosa 117 12.0 55.6 32.5 22.2 16.7 56.3 27.1 6.40 0.59 1.18 1.00 17.93 5.84 4.24
Amazona autumnalis * 176 17.6 59.1 23.3 26.9 24.1 48.3 27.6 10.55 0.86 1.25 0.72 10.89 0.95 50.59
Deroptyus accipitrinus * 47 4.3 70.2 25.5 15.7 5.9 67.6 26.5 14.19 0.21 1.49 0.79 59.11 6.40 56.64
CUCULIDAE
Piaya melanogaster 27 14.8 33.3 51.9 10.6 17.4 39.1 43.5 4.34 0.73 0.71 1.60 0.00 6.65 19.36
STRINGIDAE
Glaucidium hardyi 32 18.8 37.5 43.8 9.7 19.0 38.1 42.9 1.88 0.92 0.79 1.35 0.16 1.67 7.91
CAPRIMULGIDAE
Lurocalis semitorquatus 68 14.7 42.6 42.6 11.6 12.0 40.0 48.0 3.50 0.72 0.90 1.32 0.79 6.95 11.05
APODIDAE
Chaetura spinicaudus 12 0.0 41.7 58.3 1.9 0.0 50.0 50.0 NA 0.00 0.88 1.80 8.57 16.80 44.36
TROCHILIDAE
Phaethornis bourcieri 78 33.3 41.0 25.6 29.2 28.6 47.6 23.8 7.24 1.64 0.87 0.79 12.78 12.16 0.31
Phaethornis superciliosus * 185 33.5 45.4 21.1 52.3 24.8 50.4 24.8 21.60 1.65 0.96 0.65 26.07 38.03 12.39
Thalurania furcata 25 36.0 36.0 28.0 10.2 36.4 36.4 27.3 3.32 1.77 0.76 0.86 6.49 4.62 0.16
TROGONIDAE
Trogon melanurus 134 15.7 50.0 34.3 33.3 20.8 43.1 36.1 1.94 0.77 1.06 1.06 4.20 3.04 0.00
Trogon viridis 136 24.3 49.3 26.5 36.6 20.3 53.2 26.6 2.65 1.19 1.04 0.82 0.97 4.40 5.49
Trogon violaceus 84 17.9 51.2 31.0 24.1 19.2 53.8 26.9 0.60 0.88 1.08 0.96 1.31 0.13 1.29
43
Espécie regt regb (%)
regv (%)
regp (%)
prest (%)
presb (%)
presv (%)
presp (%)
G wb wv wp X2bv X2
bp X2vp
Trogon rufus 66 15.2 51.5 33.3 20.4 18.2 52.3 29.5 1.24 0.74 1.09 1.03 3.06 1.45 0.26
MOMOTIDAE
Momotus momota 167 24.0 44.9 31.1 31.5 20.6 50.0 29.4 1.27 1.18 0.95 0.96 2.59 1.85 0.01
GALBULIDAE
Galbula albirostris 115 20.0 44.3 35.7 37.0 20.0 41.3 38.8 0.57 0.98 0.94 1.10 0.07 0.41 2.43
Galbula leucogastra 13 38.5 30.8 30.8 4.6 50.0 40.0 10.0 2.51 1.89 0.65 0.95 4.73 2.76 0.66
Galbula dea * 183 10.9 50.8 38.3 45.4 16.3 46.9 36.7 11.96 0.54 1.08 1.18 23.05 22.79 2.39
Jacamerops aureus 21 23.8 42.9 33.3 6.9 26.7 33.3 40.0 0.21 1.17 0.91 1.03 0.43 0.10 0.23
BUCCONIDAE
Notharchus macrorhynchos 19 5.3 52.6 42.1 6.5 7.1 50.0 42.9 3.65 0.26 1.11 1.30 7.99 7.22 1.19
Bucco tamatia * 33 6.1 36.4 57.6 7.4 12.5 37.5 50.0 10.72 0.30 0.77 1.78 3.78 27.34 53.30
Monasa atra 39 30.8 46.2 23.1 11.6 28.0 52.0 20.0 2.96 1.51 0.98 0.71 3.36 5.32 1.94
CAPITONIDAE
Capito niger 52 13.5 51.9 34.6 18.1 12.8 53.8 33.3 1.70 0.66 1.10 1.07 4.05 2.43 0.06
RAMPHASTIDAE
Ramphastos tucanus 187 18.2 50.3 31.6 20.8 13.3 62.2 24.4 0.86 0.89 1.06 0.97 1.97 0.31 1.40
Ramphastos vitellinus 53 15.1 50.9 34.0 12.0 19.2 57.7 23.1 0.99 0.74 1.08 1.05 2.32 1.36 0.05
Selenidera piperivora 89 9.0 55.1 36.0 26.9 13.8 46.6 39.7 8.60 0.44 1.17 1.11 22.77 12.48 0.40
Pteroglossus viridis 12 8.3 16.7 75.0 3.2 14.3 28.6 57.1 9.15 0.41 0.35 2.31 0.02 25.14 59.60
PICIDAE
Melanerpes cruentatus 39 5.1 56.4 38.5 13.0 7.1 53.6 39.3 7.44 0.25 1.19 1.19 20.71 12.06 0.00
Veniliornis cassini 51 9.8 58.8 31.4 19.9 11.6 55.8 32.6 4.83 0.48 1.25 0.97 14.77 3.72 5.43
Piculus flavigula 89 23.6 40.4 36.0 30.6 19.7 40.9 39.4 1.67 1.16 0.86 1.11 2.36 0.05 4.15
Piculus chrysochloros 19 15.8 36.8 47.4 7.9 17.6 35.3 47.1 1.83 0.78 0.78 1.46 0.00 2.70 7.76
Celeus undatus 133 12.0 53.4 34.6 31.5 11.8 58.8 29.4 6.55 0.59 1.13 1.07 16.63 8.77 0.62
Celeus torquatus 28 0.0 42.9 57.1 10.2 0.0 40.9 59.1 NA 0.00 0.91 1.76 21.00 37.33 89.33
44
Espécie regt regb (%)
regv (%)
regp (%)
prest (%)
presb (%)
presv (%)
presp (%)
G wb wv wp X2bv X2
bp X2vp
Campephilus rubricollis 62 12.9 50.0 37.1 19.9 16.3 53.5 30.2 2.45 0.63 1.06 1.14 4.51 4.66 0.50
THAMNOPHILIDAE
Terenura spodioptila 62 17.7 38.7 43.5 21.3 19.6 43.5 37.0 3.38 0.87 0.82 1.34 0.05 3.94 14.17
Myrmeciza ferruginea * 140 10.0 47.9 42.1 32.4 12.9 48.6 38.6 12.86 0.49 1.01 1.30 16.67 28.55 14.32
Epinecrophylla gutturalis 28 21.4 32.1 46.4 10.2 22.7 31.8 45.5 3.03 1.05 0.68 1.43 1.07 1.19 11.34
Myrmotherula brachyura 255 23.9 45.5 30.6 45.4 25.5 45.9 28.6 1.92 1.17 0.96 0.94 3.51 3.23 0.07
Myrmotherula guttata 12 8.3 50.0 41.7 5.1 0.0 54.5 45.5 1.41 0.41 1.06 1.29 2.42 2.97 0.87
Myrmotherula axillaris 46 17.4 45.7 37.0 17.1 18.9 43.2 37.8 0.52 0.85 0.97 1.14 0.20 1.01 1.23
Myrmotherula longipennis 95 18.9 46.3 34.7 30.6 21.2 48.5 30.3 0.27 0.93 0.98 1.07 0.08 0.49 0.67
Myrmotherula menetriesii 112 18.8 42.0 39.3 29.6 20.3 46.9 32.8 2.36 0.92 0.89 1.21 0.04 2.57 9.70
Thamnomanes ardesiacus 188 21.8 40.4 37.8 39.8 23.3 46.5 30.2 3.70 1.07 0.86 1.17 2.58 0.44 13.60
Thamnomanes caesius 313 21.1 43.5 35.5 60.2 20.0 49.2 30.8 1.92 1.04 0.92 1.09 1.29 0.28 7.49
Herpsilochmus dorsimaculatus 750 21.1 49.7 29.2 75.5 19.6 50.9 29.4 3.62 1.03 1.05 0.90 0.09 3.23 14.57
Thamnophilus murinus * 369 11.7 49.1 39.3 51.9 15.2 50.0 34.8 21.62 0.57 1.04 1.21 33.30 44.98 12.77
Cymbilaimus lineatus 110 16.4 51.8 31.8 31.5 20.6 45.6 33.8 1.42 0.80 1.10 0.98 3.58 0.93 1.42
Frederickena viridis 23 21.7 21.7 56.5 7.4 25.0 31.3 43.8 7.36 1.07 0.46 1.74 2.18 3.88 25.32
Schistocichla leucostigma 11 54.5 36.4 9.1 4.2 44.4 44.4 11.1 7.19 2.68 0.77 0.28 13.86 12.02 1.89
Percnostola rufifrons 205 19.5 42.0 38.5 35.2 21.1 42.1 36.8 3.57 0.96 0.89 1.19 0.31 2.91 15.02
Cercomacra cinerascens * 291 15.1 55.7 29.2 40.7 15.9 50.0 34.1 9.43 0.74 1.18 0.90 22.65 1.98 23.59
Hypocnemis cantator 234 16.2 50.0 33.8 45.8 19.2 50.5 30.3 2.61 0.80 1.06 1.04 5.91 3.74 0.07
Pithys albifrons 42 38.1 35.7 26.2 16.7 33.3 36.1 30.6 6.97 1.87 0.76 0.81 13.42 10.52 0.07
Willisornis poecilinotus 125 20.0 52.8 27.2 34.3 17.6 44.6 37.8 1.91 0.98 1.12 0.84 0.84 0.62 8.63
Gymnopithys rufigula 68 22.1 54.4 23.5 24.1 21.2 50.0 28.8 2.63 1.08 1.15 0.73 0.12 2.02 10.72
GRALLARIDAE
Grallaria varia 47 25.5 36.2 38.3 8.3 38.9 27.8 33.3 2.37 1.25 0.77 1.18 3.02 0.06 5.51
Hylopezus macularius 13 0.0 23.1 76.9 3.2 0.0 42.9 57.1 NA 0.00 0.49 2.37 3.90 43.33 277.65
45
Espécie regt regb (%)
regv (%)
regp (%)
prest (%)
presb (%)
presv (%)
presp (%)
G wb wv wp X2bv X2
bp X2vp
Myrmothera campanisona 23 39.1 43.5 17.4 6.5 35.7 42.9 21.4 5.12 1.92 0.92 0.54 6.96 8.73 2.29
FORMICARIDAE
Formicarius colma 171 17.5 48.0 34.5 42.6 20.7 43.5 35.9 0.95 0.86 1.02 1.06 1.42 1.86 0.37
Formicarius analis * 84 32.1 50.0 17.9 22.2 27.1 43.8 29.2 11.55 1.58 1.06 0.55 7.46 18.03 16.03
SCRERURIDAE
Sclerurus mexicanus 18 16.7 44.4 38.9 6.9 20.0 40.0 40.0 0.38 0.82 0.94 1.20 0.09 0.72 1.09
Sclerurus rufigularis 22 31.8 54.5 13.6 7.9 35.3 47.1 17.6 4.51 1.56 1.16 0.42 1.33 5.69 9.41
DENDROCOLAPTIDAE
Dendrocincla fuliginosa 118 27.1 37.3 35.6 34.7 21.3 42.7 36.0 5.40 1.33 0.79 1.10 9.33 1.59 7.51
Dendrocincla merula 18 44.4 27.8 27.8 6.5 35.7 35.7 28.6 5.63 2.18 0.59 0.86 10.67 7.67 0.72
Deconychura longicauda 50 10.0 54.0 36.0 16.2 11.4 57.1 31.4 3.91 0.49 1.14 1.11 9.86 5.87 0.07
Sittassomus griseicapillus 167 17.4 45.5 37.1 44.4 17.7 50.0 32.3 1.98 0.85 0.96 1.15 0.71 3.85 4.90
Certhiasomus stictolaemus 22 27.3 36.4 36.4 8.3 22.2 38.9 38.9 1.16 1.34 0.77 1.12 1.90 0.26 1.80
Glyphorynchus spirurus 222 24.3 47.7 27.9 56.0 24.8 46.3 28.9 3.06 1.19 1.01 0.86 2.37 5.71 3.83
Xiphorhynchus pardalotus 459 18.1 47.1 34.9 70.8 20.9 46.4 32.7 2.06 0.89 1.00 1.08 1.86 4.24 2.53
Campylorhamphus procurvoides 41 19.5 36.6 43.9 12.5 18.5 40.7 40.7 2.58 0.96 0.77 1.35 0.41 1.83 10.72
Lepidocolaptes albolineatus * 45 6.7 40.0 53.3 16.7 8.3 41.7 50.0 11.24 0.33 0.85 1.65 6.01 28.02 43.62
Dendrexetastes rufigula 21 9.5 42.9 47.6 7.9 11.8 41.2 47.1 2.91 0.47 0.91 1.47 1.75 6.85 8.22
Dendrocolaptes certhia 102 9.8 50.0 40.2 19.4 7.1 54.8 38.1 8.87 0.48 1.06 1.24 15.24 18.22 4.29
Dendrocolaptes picumnus 41 9.8 46.3 43.9 14.8 9.4 46.9 43.8 4.32 0.48 0.98 1.35 4.51 9.92 7.13
Hylexetastes perrotii 54 14.8 51.9 33.3 14.4 12.9 54.8 32.3 1.16 0.73 1.10 1.03 2.90 1.34 0.27
FURNARIIDAE
Xenops minutus 43 11.6 51.2 37.2 15.7 11.8 50.0 38.2 2.34 0.57 1.08 1.15 4.79 4.23 0.21
Automolus infuscatus 53 17.0 56.6 26.4 18.5 17.5 57.5 25.0 1.87 0.83 1.20 0.82 2.82 0.00 7.81
Philydor erythrocercum 20 20.0 25.0 55.0 7.9 23.5 17.6 58.8 5.13 0.98 0.53 1.70 1.12 3.55 19.47
Philydor pyrrhodes * 17 58.8 23.5 17.6 7.4 62.5 18.8 18.8 11.99 2.89 0.50 0.54 25.77 22.90 0.02
46
Espécie regt regb (%)
regv (%)
regp (%)
prest (%)
presb (%)
presv (%)
presp (%)
G wb wv wp X2bv X2
bp X2vp
PIPRIDAE
Tyranneutes virescens 222 23.9 44.6 31.5 50.0 23.1 40.7 36.1 1.65 1.17 0.94 0.97 3.51 2.12 0.14
Pipra erythrocephala * 213 19.2 38.5 42.3 37.5 16.0 43.2 40.7 9.61 0.94 0.82 1.30 1.10 7.61 40.38
Lepidothrix serena 78 19.2 46.2 34.6 19.9 20.9 44.2 34.9 0.18 0.94 0.98 1.07 0.03 0.31 0.54
Dixiphia pipra * 56 51.8 32.1 16.1 16.2 40.0 40.0 20.0 27.64 2.54 0.68 0.50 54.91 49.06 1.20
Corapipo gutturalis 108 16.7 49.1 34.3 33.3 19.4 50.0 30.6 0.97 0.82 1.04 1.06 1.91 1.64 0.03
TITYRIDAE
Onychorhynchus coronatus 12 41.7 33.3 25.0 3.7 25.0 37.5 37.5 2.81 2.05 0.71 0.77 5.43 4.34 0.03
Terenotriccus erythrurus 44 18.2 40.9 40.9 17.1 18.9 40.5 40.5 1.40 0.89 0.87 1.26 0.01 1.66 5.76
Schiffornis turdina 83 25.3 44.6 30.1 24.5 22.6 47.2 30.2 1.18 1.24 0.94 0.93 2.22 1.91 0.01
Laniocera hypopyrra 14 28.6 42.9 28.6 5.6 25.0 50.0 25.0 0.53 1.40 0.91 0.88 0.99 0.87 0.01
Tityra cayana 20 10.0 40.0 50.0 8.8 10.5 42.1 47.4 3.17 0.49 0.85 1.54 1.05 7.29 11.49
Pachyramphus marginatus 36 19.4 27.8 52.8 12.0 23.1 38.5 38.5 7.27 0.95 0.59 1.63 1.36 5.38 28.72
Pachyramphus surinamus 35 0.0 31.4 68.6 13.0 0.0 35.7 64.3 NA 0.00 0.67 2.12 16.04 76.36 364.54
COTINGIDAE
Lipaugus vociferans 855 22.7 46.2 31.1 33.8 16.4 54.8 28.8 2.82 1.11 0.98 0.96 4.97 4.90 0.22
Xipholena punicea 77 15.6 49.4 35.1 27.3 16.9 47.5 35.6 1.18 0.77 1.05 1.08 2.24 2.10 0.10
Perissocephalus tricolor * 36 52.8 22.2 25.0 5.6 33.3 50.0 16.7 19.44 2.59 0.47 0.77 37.27 31.41 1.78
Phoenicircus carnifex 115 27.0 48.7 24.3 31.5 25.0 51.5 23.5 4.80 1.32 1.03 0.75 3.13 8.34 6.80
TYRANNOIDEA
Platyrinchus coronatus 38 26.3 42.1 31.6 12.5 25.9 40.7 33.3 0.83 1.29 0.89 0.97 1.76 0.91 0.18
Platyrinchus platyrhynchos 28 17.9 42.9 39.3 11.6 16.0 44.0 40.0 0.59 0.88 0.91 1.21 0.01 0.86 2.22
Piprites chloris 158 13.3 53.2 33.5 38.4 15.7 51.8 32.5 5.63 0.65 1.13 1.04 14.60 6.52 1.43
RHYNCHOCYCLIDAE
Mionectes macconnelli 33 39.4 33.3 27.3 8.8 26.3 47.4 26.3 6.38 1.93 0.71 0.84 12.38 8.97 0.36
Corythopis torquatus * 41 34.1 58.5 7.3 8.8 36.8 52.6 10.5 15.84 1.68 1.24 0.23 3.32 16.25 35.88
47
Espécie regt regb (%)
regv (%)
regp (%)
prest (%)
presb (%)
presv (%)
presp (%)
G wb wv wp X2bv X2
bp X2vp
Phylloscartes virescens 42 11.9 38.1 50.0 16.2 14.3 40.0 45.7 5.97 0.58 0.81 1.54 0.79 12.43 23.99
Tolmomyias assimilis 296 18.6 43.6 37.8 55.6 20.8 42.5 36.7 3.89 0.91 0.92 1.17 0.01 5.14 14.93
Tolmomyias poliocephalus 23 8.7 43.5 47.8 10.2 9.1 45.5 45.5 3.50 0.43 0.92 1.48 2.52 8.35 9.32
Todirostrum pictum 21 9.5 57.1 33.3 7.9 5.9 52.9 41.2 1.93 0.47 1.21 1.03 5.67 2.05 0.95
Myiornis ecaudatus 50 12.0 46.0 42.0 17.6 13.2 47.4 39.5 3.33 0.59 0.97 1.30 2.95 7.49 5.70
Hemitriccus zosterops * 389 10.3 44.0 45.8 67.6 15.8 45.9 38.4 43.64 0.50 0.93 1.41 29.57 101.28 107.28
Lophotriccus vitiosus * 192 40.1 40.1 19.8 41.7 27.8 48.9 23.3 41.68 1.97 0.85 0.61 68.32 72.33 7.04
TYRANNIDAE
Zimmerius gracilipes 345 18.0 51.9 30.1 72.2 18.6 49.4 32.1 3.11 0.88 1.10 0.93 5.92 0.20 9.07
Ornithion inerme 25 20.0 56.0 24.0 9.3 20.0 60.0 20.0 0.99 0.98 1.19 0.74 0.40 0.35 4.57
Myiopagis gaimardii 157 28.0 49.0 22.9 44.9 24.7 49.5 25.8 9.00 1.38 1.04 0.71 5.71 15.25 12.90
Myiopagis caniceps 28 28.6 39.3 32.1 9.7 19.0 47.6 33.3 1.22 1.40 0.83 0.99 2.50 1.12 0.48
Tyrannulus elatus 70 20.0 58.6 21.4 17.6 21.1 52.6 26.3 4.74 0.98 1.24 0.66 1.86 1.73 22.34
Attila spadiceus 66 13.6 42.4 43.9 19.0 17.1 39.0 43.9 4.43 0.67 0.90 1.36 1.27 9.23 13.75
Ramphotrigon ruficauda 21 14.3 66.7 19.0 6.5 21.4 57.1 21.4 3.28 0.70 1.41 0.59 5.39 0.08 17.71
Sirystes sibilator 54 11.1 48.1 40.7 20.8 13.3 44.4 42.2 3.81 0.55 1.02 1.26 5.09 8.27 3.56
Rhytipterna simplex 151 14.6 51.7 33.8 40.7 17.0 52.3 30.7 3.47 0.72 1.09 1.04 8.47 4.44 0.42
Conopias parvus 100 14.0 49.0 37.0 24.1 17.3 44.2 38.5 2.93 0.69 1.04 1.14 4.73 5.81 1.12
VIREONIDAE
Cyclarhis gujanensis 32 9.4 46.9 43.8 9.3 10.0 55.0 35.0 3.53 0.46 0.99 1.35 4.03 8.05 5.24
Vireolanius leucotis * 199 10.1 48.2 41.7 40.7 9.1 52.3 38.6 17.72 0.49 1.02 1.29 24.24 38.88 17.41
Hylophilus thoracicus 10 40.0 50.0 10.0 3.7 25.0 62.5 12.5 3.62 1.96 1.06 0.31 2.98 5.05 4.25
Hylophilus muscicapinus 377 15.4 50.1 34.5 62.5 13.3 50.4 36.3 6.18 0.76 1.06 1.06 13.33 9.77 0.00
Hylophilus ochraceiceps * 109 13.8 37.6 48.6 26.4 15.8 40.4 43.9 12.59 0.68 0.80 1.50 0.56 23.09 51.89
TROGLODYTIDAE
Microcerculus bambla 24 12.5 54.2 33.3 10.2 13.6 50.0 36.4 1.09 0.61 1.15 1.03 2.92 1.18 0.39
48
Espécie regt regb (%)
regv (%)
regp (%)
prest (%)
presb (%)
presv (%)
presp (%)
G wb wv wp X2bv X2
bp X2vp
Cyphorhinus arada 23 4.3 60.9 34.8 8.3 5.6 55.6 38.9 5.15 0.21 1.29 1.07 17.50 6.29 2.32
POLIOPTILIDAE
Microbates collaris 95 14.7 40.0 45.3 28.7 16.1 41.9 41.9 7.05 0.72 0.85 1.40 0.50 12.79 26.84
Ramphocaenus melanurus 40 27.5 50.0 22.5 14.4 29.0 51.6 19.4 2.32 1.35 1.06 0.69 1.11 3.75 4.05
TURDIDAE
Turdus albicollis 119 16.0 50.4 33.6 30.6 18.2 50.0 31.8 1.53 0.78 1.07 1.04 3.58 2.05 0.11
THRAUPIDAE
Coereba flaveola * 97 57.7 28.9 13.4 28.2 49.2 34.4 16.4 66.15 2.83 0.61 0.41 143.64 122.44 2.49
Saltator grossus 60 21.7 53.3 25.0 16.2 20.0 51.4 28.6 1.61 1.06 1.13 0.77 0.09 1.21 6.63
Lamprospiza melanoleuca 93 29.0 38.7 32.3 22.2 20.8 41.7 37.5 4.54 1.43 0.82 1.00 9.23 4.08 1.90
Lanio fulvus * 80 1.3 41.3 57.5 17.6 2.6 44.7 52.6 38.25 0.06 0.87 1.77 41.32 97.54 206.82
Lanio surinamus * 102 41.2 36.3 22.5 27.3 35.6 39.0 25.4 22.92 2.02 0.77 0.70 42.51 38.24 0.33
Tangara chilensis * 22 9.1 13.6 77.3 5.1 18.2 27.3 54.5 18.86 0.45 0.29 2.38 0.24 53.82 117.37
Tangara varia 39 23.1 43.6 33.3 16.2 22.9 42.9 34.3 0.26 1.13 0.92 1.03 0.52 0.10 0.33
EMBEREZIDAE
Arremon taciturnus 14 35.7 28.6 35.7 6.5 35.7 28.6 35.7 2.57 1.75 0.61 1.10 4.27 1.54 1.96
CARDINALIDAE
Caryothraustes canadensis 165 23.6 45.5 30.9 37.0 21.3 45.0 33.8 1.05 1.16 0.96 0.95 2.00 1.69 0.01
Cyanoloxia cyanoides 9 55.6 33.3 11.1 4.2 55.6 33.3 11.1 5.80 2.73 0.71 0.34 11.88 10.19 0.82
ICTERIDAE
Psarocolius viridis 39 7.7 59.0 33.3 10.2 9.1 63.6 27.3 5.11 0.38 1.25 1.03 15.98 5.42 3.00
Cacicus haemorrhous 47 23.4 51.1 25.5 16.7 16.7 52.8 30.6 1.09 1.15 1.08 0.79 0.07 1.42 3.30
FRINGILIDAE
Euphonia cayennensis * 76 35.5 39.5 25.0 26.9 32.8 41.4 25.9 9.42 1.74 0.84 0.77 17.05 15.58 0.20
49
Conclusões
Apesar de nossas tentativas de explicar os resultados de forma post hoc, com base em
características observadas de cada espécie, em geral a classificação das espécies por habitats
topográficos deve ser interpretada como hipótese a ser testada em mais detalhes. Os
resultados mais fortes são que nenhuma espécie de ave mostrou especialização por classe
topográfica e que a maioria dos casos de preferência pode ser explicada como preferência por
algum microhabitat específico, associado indiretamente à topografia. Assim, concluímos que
classes topográficas em si não representam uma subdivisão da floresta com forte poder de
explicação da distribuição local das espécies de aves. No entanto, o gradiente altitudinal
dentro da floresta se associa a características relevantes para muitas espécies.
50
Apêndices
Ata da Aula de Qualificação
51
Pareceres da banca do trabalho escrito
52
53
Ata da defesa oral pública
