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ENVIRONMENTAL HETEROGENEITY AND THE ORIGIN AND MAINTENANCE OF REGIONAL AVIAN DIVERSITY IN WESTERN AMAZONIA
By
JUDIT UNGVARI-MARTIN
A DISSERTATION PRESENTED TO THE GRADUATE SCHOOL OF THE UNIVERSITY OF FLORIDA IN PARTIAL FULFILLMENT
OF THE REQUIREMENTS FOR THE DEGREE OF DOCTOR OF PHILOSOPHY
UNIVERSITY OF FLORIDA
2016
© 2016 Judit Ungvari-Martin
To my trusty external hard drive
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ACKNOWLEDGMENTS
I want to thank my parents in my mother tongue: Köszönöm szépen a rengeteg
türelmet! Additionally, I also want to thank my committee for their patience, especially J.
Gordon Burleigh who pushed me through when needed, Scott K. Robinson who
supported me throughout this arduous but fun journey, Bette Loiselle, David Reed and
Per Lundberg who made themselves available during the short periods of time when
everyone could be around and provided valuable knowledge, feedback and support. Kitt
Heckscher and Keith Hobson were essential for the successful publication of the Grey-
cheeked Thrush data. The funding sources were the following: the Wilson Ornithological
Society, the Cooper Ornithological Society, Sigma Xi, the American Ornithologists
Union, the Florida Museum of Natural History Ordway Endowment, and the National
Science Foundation Graduate Research Fellowship (DGE-0802270). SERNANP
granted permits to perform the fieldwork and data collections. Information in Chapter 2
was provided by NatureServe (www.natureserve.org) and its network of natural heritage
member programs, a leading source of information about rare and endangered species,
and threatened ecosystems. I must thank immensely my family near and far, my lab
mates, my friends, loved ones, and the counseling center who kept me borderline sane,
and my pets who provided the much needed affection. Families in the communities of El
Dorado, Nueva Esperanza, San Martin, Mishana, Yuto and Porvenir hosted our field
crews for weeks on end, and fed us nourishing meals made of simple ingredient.
Throughout the four years of fieldwork, 44 field assistants helped out in Peru with data
collections, without their sweat and tears this work would not have been possible.
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TABLE OF CONTENTS page
ACKNOWLEDGMENTS .................................................................................................. 4
LIST OF TABLES ............................................................................................................ 7
LIST OF FIGURES .......................................................................................................... 9
LIST OF ABBREVIATIONS ........................................................................................... 12
ABSTRACT ................................................................................................................... 13
CHAPTER
1 MOTIVATION ......................................................................................................... 15
Overview ................................................................................................................. 15
Outline .................................................................................................................... 18 Purpose .................................................................................................................. 20
2 PATTERNS OF SPECIES CO-OCCURRENCE FROM A PHYLOGENETIC PERSPECTIVE ....................................................................................................... 22
Introduction ............................................................................................................. 22
Methods .................................................................................................................. 27
Species Lists .................................................................................................... 27
Species Composition Comparisons .................................................................. 28 Phylogenetic Composition Comparisons .......................................................... 29
Results .................................................................................................................... 31 Species richness .............................................................................................. 31 Betadiversity ..................................................................................................... 32
Phylobetadiversity ............................................................................................ 32 Discussion .............................................................................................................. 35
3 STRUCTURE OF UNDERSTORY BIRDS COMMUNITIES IN FORESTS ON TWO CONTRASTING SOIL TYPES IN AMAZONIAN PERU ................................. 50
Introduction ............................................................................................................. 50
Methods .................................................................................................................. 54 Study Region .................................................................................................... 54 Bird Surveys ..................................................................................................... 55 Avian Guild Classification ................................................................................. 55 Data Summary and Analyses ........................................................................... 57
Results .................................................................................................................... 59 Community-wide Patterns ................................................................................ 59 Vegetation Structure ......................................................................................... 62
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Avian Guilds ..................................................................................................... 63
Discussion .............................................................................................................. 64
4 MOVEMENT, ISOLATION AND GENE FLOW AMONG PATCHES OF WHITE SAND FORESTS .................................................................................................... 86
Introduction ............................................................................................................. 86 Methods .................................................................................................................. 90
Study Landscape .............................................................................................. 90
Species-area Relationships .............................................................................. 90 Molecular Analysis ........................................................................................... 90 Population structure analysis ............................................................................ 92
Results .................................................................................................................... 94 Movements detected by recaptures ................................................................. 94
Species – area relationships ............................................................................ 95 Genetic diversity and population assignment tests ........................................... 95
Discussion .............................................................................................................. 96
5 INTER-ANNUAL SITE FIDELITY AND BREEDING ORIGINS OF GRAY-CHEEKED THRUSHES IN WHITE SAND FORESTS OF THE PERUVIAN AMAZON .............................................................................................................. 107
Introduction ........................................................................................................... 107
Methods ................................................................................................................ 109 Study Site ....................................................................................................... 109
Field Sampling ................................................................................................ 109 Stable Isotope Analysis .................................................................................. 111
Isotopic Assignment ....................................................................................... 112 Results .................................................................................................................. 112
Discussion ............................................................................................................ 114
6 CONCLUDING REMARKS ................................................................................... 129
APPENDIX
A LIST OF SPECIES CAPTURED IN AMNR FORESTS ......................................... 133
B SUMMARY OF RICHNESS ESTIMATORS .......................................................... 138
C BETWEEN-YEAR RETURN RATES IN WSF AND CLAY FOREST .................... 139
D SUPPLEMENTARY INFORMATION OF DDRADSEQ ANALYSIS ...................... 141
LIST OF REFERENCES ............................................................................................. 148
BIOGRAPHICAL SKETCH .......................................................................................... 168
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LIST OF TABLES
Table page 2-1 Ecoregions and habitat types used in this study ................................................. 42
2-2 Summary of predictions of the four hypotheses tested. ...................................... 43
3-1 Vegetation structure environmental variables used for comparisons of the four habitat types. All values represent mean values for habitat types ± standard deviations ............................................................................................ 71
3-2 Dietary guild comparisons of the four habitat types. We assessed community composition using broad dietary categories and no differences were found in the trophic organization of the understory bird community. ................................ 72
3-3 Capture rate comparisons for all birds and for select guilds or functional groups in the four habitat types using individuals per 500 net hour units. All values represent mean number of individual birds ............................................. 73
3-4 Diversity indices comparisons of bird communities of the four habitat types for all species and for white sand forest specialists alone from the AMNR capture data........................................................................................................ 74
4-1 The twenty most captured bird species, and the recaptures and movement proportions of those species. Movements are defined as recaptures on a different netline. .................................................................................................. 99
4-2 Localities, area of WSF patches, distances to nearest patch, and area of nearest patch to the sampled patch. Lat/lon coordinates are in UTM geographic coordinate system, UTM 18S. ........................................................ 100
4-3 Model selection results of the explanatory values of area of WSF patches, distances to nearest patch, and area of nearest patch to the sampled patch. .. 101
4-4 Summary of sampling locality, samples in the locality, and summary of raw sequence information for those samples. ......................................................... 102
4-5 Summary of population differentiation results based on FST calculations. ........ 103
5-1 Captured and recaptured Gray-cheeked Thrushes (Catharus minimus) in Amazonian white sand forests (wsf) and clay terra firme forests (clay) in Allpahuayo-Mishana National Reserve ............................................................. 120
A-1 List of species captured in forest in Allpahuayo-Mishana during 2009-2012 with raw capture numbers for each species ..................................................... 133
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B-1 Incidence-based richness estimators with standard deviations of the four different habitat types in AMNR. ....................................................................... 138
C-1 Comparison of return rates for all species with a minimum of 5 captures. All values represent mean number of individual birds in the habitat types ± standard deviations. Habitat specialists are indicated in the last column. ........ 139
D-1 Run metrics summary from the High throughput NextSeq 500 run of single end 150 bp reads. ............................................................................................. 142
D-2 Raw sequencing information for samples after quality filtering steps from the catalog of RAD loci. .......................................................................................... 143
D-3 Summary statistics for the 129 individuals included in the final filtered dataset, 2240 biallelic loci included in the dataset. ........................................... 147
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LIST OF FIGURES
Figure page 1-1 The edge of the Nueva Esperanza (AMNR, Loreto, Peru) field site in 2011,
where forest stood in previous years. Photo courtesy of Cristian Gallego Carmona. ............................................................................................................ 21
2-1 Map of the three ecoregions used for assembling the regional communities. The labeled and outlined ecoregions all belong to the moist tropical forest biome. ................................................................................................................. 44
2-2 Network representation of the cluster of communities that are taxonomically more similar to each other than expected by chance. The color of the spheres represents the ecoregion (red=NAPO, blue=SW, black=GUI). ............. 45
2-3 Cluster dendrograms using taxonomic betadiversity and phylobetadiversity for all local bird communities in different habitats across the three ecoregions .. 46
2-4 Clusters of communities in habitats that are phylogenetically more similar to each other than expected by chance. This network representation of all the phylobetadiversity comparison of the 33 habitats in 3 ecoregions. ..................... 47
2-5 Within-ecoregion betadiversity phylogenetic betadiversity. A) Betadiversity results using taxonomic turnover. B) Phylogenetic betadiversity results within each ecoregion. .................................................................................................. 48
2-6 Relationship between phylogenetic betadiversity (PBD, black points) betadiversity (BC, blue points), and species richness. The lines represent best fit lines corresponding to linear regressions. ............................................... 49
3-1 Comparison of number of species between two general habitat types. Slightly more species were captured in clay soil forests than in white sands forests per sampling event (t-test, p= 0.01043) .............................................................. 75
3-2 Rank abundance curves for white sand forests and clay forest captures. Species are arranged according to their rank order abundance in clay soil forest, since terra firme is the dominant forest type. ........................................... 76
3-3 Forest canopy height in the four different habitat types, letter codes refer to statistical differences (ANOVA, p<0.05), red points indicate outlier values, letter codes a-b-c-d indicate statistically different values. ................................... 77
3-4 Cumulative distribution of species occurrences over all netlines (n=93 sampling events). Each point represent a species, and the number of sampling events at which that species was recorded. ........................................ 78
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3-5 Species accumulation curves for four different habitat types. Rarefaction analysis was performed to standardize comparisons with effort. ........................ 79
3-6 Turnover, dissimilarity and nestedness between white sand and clay soil forest bird communities. ..................................................................................... 80
3-7 Ordination diagram based on Bray-Curtis Dissimilarity using netlines as sites for all WSF and clay habitats. The grey lines connecting the points result from the hierarchical clustering analysis using the average linkage method. ..... 81
3-8 Comparison of capture rates and number of observed species of flocking or non-flocking shrub gleaner insectivores. A) Capture rates B) Observed number of species per guild in each habitat type ................................................ 82
3-9 Comparisons of captures and species according to foraging methods. A) Capture rates B) The observed number of species per guild ............................. 83
3-10 Comparisons of captures and species number based on dietary categories. A) Capture rates of nectarivores and frugivores B) The observed number of species per guild. ................................................................................................ 84
3-11 Rarefaction based species accumulation curves show the differences for white sand forest habitats and clay habitat. Overlapping 95% confidence intervals are interpreted as no difference. .......................................................... 85
4-1 Map of white sand forest patches including the 150 m buffer from the edge for each forest island. The names of the putatively isolated regions are included on the map. ........................................................................................ 104
4-2 The relationship between species richness and the area of the sampled white sand patch, the area of the nearest WSF patch and the distance to the nearest WSF patch. Blue points represent WSF specialist species. ................ 105
4-3 Population genetics results based on k-means clustering as well as principal component analysis based on allele frequencies indicate little differentiation between regional populations, and a single genetic cluster. ............................. 106
5-1 Location of Allpahuayo-Mishana National Reserve (outline), Department of Loreto, southeast of Iquitos, Peru. White sand forests are the small polygons outlined in black (image source: Google Earth). ............................................... 124
5-2 Weekly numbers of Gray-cheeked Thrushes by age caught using constant effort mistnet transects at Allpahuayo-Mishana National Reserve, Department of Loreto, Peru, 13 October 2010 to 8 December 2012. ............... 125
5-3 Cumulative number of Gray-cheeked Thrushes (Catharus minimus) and Swainson’s Thrushes (C. ustulatus) captured over a nine week period in 2010, 2011, and 2012 at Allpahuayo-Mishana National Reserve ..................... 126
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5-4 Cumulative recaptures by week for Gray-cheeked Thrushes using constant effort mistnet transects at Allpahuayo-Mishana National Reserve, Department of Loreto, Peru. ............................................................................. 127
5-5 Depictions of likely breeding or natal origins of Gray-cheeked Thrushes at Allpahuayo-Mishana National Reserve, Department of Loreto, Peru. The spatially explicit assignment of the population (n=12)....................................... 128
A-1 Comparisons of WSF and clay and the four specific habitat types by capture rate. Neither A) Capture rates between clay and WSF nor B) captures rates among the four specific habitat types in AMNR differed. .................................. 137
B-1 Graphical representation of incidence based richness estimators with confidence intervals for all sampling localities and habitats combined. ............ 138
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LIST OF ABBREVIATIONS
AMNR Allpahuayo Mishana National Reserve
BCD Bray-Curtis dissimilarity
bp Base pairs
c.i. Confidence Interval
CC Cocha Cashu
dbh Diameter at breast height
ddRADseq Double Digest RAD Sequencing
Gbp Giga base pairs
GUI Guianan Moist Forest Ecoregion
HWE Hardy - Weinberg Equilibrium
km kilometer
LD Linkage Disequilibrium
m Meter
MCMC Markov chain Monte Carlo
ML Maximum Likelihood
PBD Phylobetadiversity
PCR Polymerase chain reaction
SD Standard Deviation
SW Southwest
WSF White sand forest
δ2H Stable hydrogen isotope
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Abstract of Dissertation Presented to the Graduate School of the University of Florida in Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy
ENVIRONMENTAL HETEROGENEITY AND THE ORIGIN AND MAINTENANCE OF
REGIONAL AVIAN DIVERSITY IN WESTERN AMAZONIA
By
Judit Ungvari-Martin
May 2016
Chair: Scott K Robinson Major: Biology
The number of species that can co-occur in a place depends on environmental
complexity and on species interactions. Although the vast region of Amazonia has a
relatively uniform climate, the rich landscape heterogeneity creates a patchwork of
habitats with different bird communities. The objective of this study was to examine the
origin and maintenance of Amazonian biodiversity. First, the distribution of bird species
was examined using taxonomic betadiversity and phylogenetic betadiversity to explore
broad-scale patterns of diversity in different habitats across ecoregions. Then, on a
landscape level I examined how habitat specialization affects patterns of species
composition and trophic structure, focusing on two contrasting habitats: clay soil forests
and white sand forests (WSF). These bird communities differed in species richness,
which may be related to habitat productivity, but the community structure was similar.
White sand forests are patchy, though widely distributed, and they occur in an
island-like landscape with different-sized areas and degrees of isolation. If species
disperse little among patches, their isolation may promote speciation. We performed a
mark-recapture study in a reserve in western Amazonia to examine the movement of
understory birds among white sand forest patches and found that 3.6% of all recaptures
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involved movements among different netlines. However, we found evidence of high
gene flow among isolated populations of an obligate WSF specialist species, which
suggests that the surrounding clay soil forests do not pose a strong barrier to dispersal
for the obligate WSF specialist Neopelma chrysocephalum on the scale of 1-10km. We
also found evidence that WSF are important for long-distance migrants, and identified
that Catharus minimus uses WSF.
Together, these data suggest that although diversity of habitats is critical to the
maintenance of the high regional species diversity of western Amazonia, communities in
forested habitats are similar in structure with the addition of a few habitat specialists and
limited turnover. Also, forested habitat matrix is permeable for some habitat specialist
species, which may slow the rates of speciation, even in seemingly isolated patches.
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CHAPTER 1 MOTIVATION
Overview
The search for patterns in the spatial organization of species diversity and the
identification of the mechanisms that underlie those patterns have long been central
concerns of community ecologists. Community ecology has undergone significant
paradigms shifts, and theory has developed faster than empirical tests (Logue et al.
2011). Ricklefs proposed the concept of “regional communities” that highlights
communities made up of populations of species that are themselves distributed across
potential ecological and geographical gradients (Ricklefs 2008). The regional
community concept, which is strongly analogous to the concept of metacommunity
(Leibold et al. 2004, Holyoak et al. 2005), has expanded the scope of community
ecology both spatially and temporally by integrating global-scale analyses with multiple
time scales that approach the realm of biogeography (Leibold et al. 2004, Ricklefs 2008,
Ricklefs 2011).
Ecoregions, defined based on large-scale patterns of floristic and zoogeographic
variation, provide broad categories of all major terrestrial, freshwater and marine habitat
types spanning all continents and oceans (Olson et al. 2001). Ecoregions provide the
context for well-defined regional communities. Local communities are the traditional
focus of community ecology; these local communities consist of a subset of the species
found within the regional species pool. Local communities within a region can vary
greatly in composition, even when they are close together. We use the concept of local
communities as the subsets of species that live in a particular habitat within the tropical
moist forest biome of Amazonia.
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The Amazon basin is over seven million square kilometers, five million of which
are covered in tropical rainforest. The region displays some variation in rainfall,
temperature and geology, and some researchers have emphasized the biogeographical
and evolutionary importance of this variation in explaining high levels of regional
diversity (Tuomisto and Ruokolainen 1997, Tuomisto 2007). An extensive river system
dominates the region, and there are well known chemical, physical and biological
differences between eutrophic, nutrient rich white-water rivers and oligotrophic, nutrient
poor black-water and clear-water rivers (Janzen 1974, Tuomisto et al. 1995).
These rivers are of great importance; they alter the soil conditions by seasonally
flooding some areas, which deposits nutrient-rich sediments (in the case of white-water
rivers) or renders the soils infertile with high humic acid concentrations (in the case of
black-water rivers), thus creating a mosaic of patches in the region (Janzen 1974).
Environmental heterogeneity produces habitat diversity in the Amazonian region, which,
in turn, contributes to regional species diversity (-diversity) (Borges 2004, Cramer and
Willig 2005, Andersen et al. 2010, Tuomisto 2010a). Within heterogeneous landscapes,
many, if not most habitat types harbor endemic plant and animal species (Steege et al.
2000, Fine et al. 2005, Ruokolainen et al. 2007).
One of the most dramatic recent discoveries is the extent to which communities
that grow on certain kinds of soils differ in their species composition. White sands
forests (hereafter WSF), in particular, have been shown to have many specialist birds
and plants (Alvarez Alonso and Whitney 2001, Fine et al. 2005, Poletto and Aleixo
2005, Fine et al. 2010, Alvarez Alonso et al. 2013, Fine and Baraloto 2016) and to be
lower in productivity than soils growing on more nutrient-rich clay soils that dominate
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most sections of the Amazon Basin (Tuomisto and Poulsen 1996, Asner et al. 2000,
Fine et al. 2006, Higgins et al. 2014). White-sand forests are patchily distributed
throughout northern South America in Brazil, Peru, Guyana, Venezuela, French Guiana,
Suriname and Colombia as well. The sediments making up these soils eroded from the
Guiana Shield and were transported westward into the basin of the Nanay River of Peru
and to the Upper Rio Negro region of Brazil during the early Miocene (Janzen 1974,
Kalliola et al. 1993). WSF soil is composed almost solely of sand and has less than 5%
clay (Anderson 1981). There are a variety of vernacular terms to describe sand-based
vegetation forms have a variety of vernacular terms describing them depending on the
regions. They are called varillales in Peru, caatingas or campinas/campinaranas
sometimes also humirizales, carrascales, chavascales or charavascales in Brazil, muri
bush or wallaba forest in Guyana, and bana, cunuri or yaguácanan in Venezuela
(Anderson 1981, Adeney et al. 2016, Daly et al. 2016).
White-sand soils are extremely nutrient-poor (Janzen 1974) and also quick-
drying because sand has little water-holding capacity. The rapid passage of water
through the soil leaches organic matter and clay particles; making the soils deficient in
minerals (Anderson 1981, Tuomisto 2006, Andersen et al. 2010). Even within the
climatically almost uniform rain forests it has been observed that very few plant species
can grow on all kinds of soil (Brown and Prance 1987, Coomes and Grubb 1996,
Benavides et al. 2006, Andersen et al. 2010, Garcia-Villacorta et al. 2016). These
nutrient-poor white sand soils that cover less than three percent of Amazonia support
resource-limited plant species that have slow growth rates. This heterogeneity in
vegetation influences the evolution and interactions among species through niche-
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based processes and habitat based biotic filtering even in the absence of strong climatic
environmental gradients (Chase and Myers 2011, Kraft et al. 2014).
Outline
Studies of community assembly seek to identify the processes that determine
how large regional species pools are filtered into local communities, and conversely,
how processes that occur in the local community affect regional biodiversity. On both
regional and local scales, community assembly is influenced by ecological processes
such as environmental heterogeneity, interspecific interactions, and dispersal limitation.
The following questions were explored in this dissertation: (1) what are the patterns of
species co-occurrence in local bird communities of the different habitat types that are
found in lowland rainforests and (2) how we can interpret and explain these patterns of
distribution in terms of ecological and evolutionary processes. The objective of this
project was to improve our understanding of the incredibly high alpha, beta and gamma
diversity of western Amazonian birds in a region that is widely considered to be the
most regionally diverse lowland avian community on earth where more than 700
species occur in lowland forests that vary little in abiotic factors (elevation, rainfall and
temperature) (Stotz 1996, Schulenberg et al. 2007).
The first specific objective was to examine the taxonomic and phylogenetic
turnover of bird assemblages at the regional spatial scale in various habitats within
different ecoregions in Amazonia (Webb et al. 2002, Cramer and Willig 2005, Gotelli
and Ulrich 2012). Understanding the turnover of species composition between habitat
types (beta diversity) within a region (gamma diversity) can help elucidate the
mechanisms driving the origin and maintenance of species diversity (Kraft et al. 2011,
Meynard et al. 2011). We used an integrated framework for linking variation in the
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species and phylogenetic composition of the ecoregions and habitats within a broader
regional community. Habitat preference plays an important role in speciation and
diversification (Futuyma and Moreno 1988, Barnagaud et al. 2011). Among birds,
specialization to a specific habitat evolved multiple times, because species from various
distant lineages can exhibit similar preferences for a certain habitat type (Terborgh et al.
1990, Kratter 1997, Alvarez Alonso and Whitney 2003).
Our second objective was to compare community composition in the WSFs of the
Peruvian Amazon to the dominant forest type of terra firme to improve our
understanding of the selective forces that may lead to the evolution of habitat
specialization. Chapter 3 discusses the regional dynamics and differentiation of bird
communities in the Allpahuayo-Mishana Natural Reserve (AMNR), where fieldwork was
conducted between 2009 and 2012. This reserve harbors some of the most extensive
systems of WSF patches in Western Amazonia and is a site in which 5 newly described
species were recently discovered in a forests less than 30 km from Iquitos, a city of
more than 500,000 people (Alvarez Alonso and Whitney 2001, Alvarez Alonso 2002,
Alvarez Alonso and Whitney 2003, Salo and Pyhala 2007, Alvarez Alonso et al. 2012).
In Chapter 4 we evaluated how patch size and isolation affected species richness
of WSF specialists whether gene flow is maintained among patches that vary widely in
their degree of isolation, and whether the degree of habitat specialization affects the
amount of gene flow among populations. In Chapter 5 we report the discovery that white
sand forests are important wintering sites for migratory thrushes. These latter chapters
focus on selected white sand forest habitat specialists.
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Purpose
Despite the overall climatic stability in the lowland tropical moist forest biome,
habitat heterogeneity contributes to the biological diversity observed in the Amazon
basin. Many endemic species are associated with just one or a few habitats such as
bamboo or Mauritia palm swamps, but other species seem to be generally associated
with habitats of lower productivity. The variation of Amazonian habitats has contributed
to the processes of speciation and diversification in the region (Brumfield and
Capparella 1996, Nores 2000, Marks et al. 2002, Fine et al. 2005, Borges 2007, Naka
2011).
WSFs had received little attention until the last decade, and their animal
communities are still not well studied. Recent studies, however, have reported new bird
species (Alonso and Whitney 2001), endemic plant species (Frasier et al. 2008, Fine
and Baraloto 2016), and the presence of restricted-range bird species in WSFs (Alonso
and Whitney 2003). WSF is a unique ecosystem that is particularly vulnerable to human
disturbance; the tough woody vegetation is desirable for construction, and nearby cities
and towns threaten the forest with increased logging, and the extraction of sand and
gravel from below ground (Fig. 1-1). Amazonian WSFs are rare and naturally
fragmented. Therefore, they should be a conservation priority for their value for
harboring unique species that increase the regional biodiversity of the lowland tropical
moist forest biome.
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Figure 1-1. The edge of the Nueva Esperanza (AMNR, Loreto, Peru) field site in 2011,
where forest stood in previous years. The forest was bulldozed away, all trees and topsoil were removed, and the sand and gravel were extracted for construction. Photo courtesy of Cristian Gallego Carmona.
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CHAPTER 2 PATTERNS OF SPECIES CO-OCCURRENCE FROM A PHYLOGENETIC
PERSPECTIVE
Introduction
The ever-expanding spatial and temporal scope of community ecology makes it
essential to consider the evolutionary processes responsible for species distributions,
which play an important role in community assembly and composition (Vellend 2010).
Applying concepts from evolutionary biology and phylogenetic approaches to
community ecology can help us understand the factors governing community assembly,
and has yielded new insights into the biodiversity of many taxa (Emerson and Gillespie
2008, Cavender-Bares et al. 2009, Graham et al. 2009, Mayfield and Levine 2010, Fine
and Kembel 2011, Kooyman et al. 2011, Peixoto et al. 2014, Weinstein et al. 2014). In
this Chapter, we seek to expand these studies to include the role of habitat
specialization in originating and maintaining regional diversity. The turnover of avian
species composition between different habitat types is governed by factors such as
landscape and vegetation (Jankowski et al. 2009, Pomara et al. 2012, Stegen 2013,
Bennett and Gilbert 2016). Various mechanisms can lead to speciation in birds, and
habitat alone generally does not (Edwards 2005), although ecological segregation
across habitats between closely related species (Veen 2010, Tene Fossog 2015) can
be a significant factor maintaining high biodiversity in small geographic regions.
In this study, we ask how habitat selection mediates the establishment of local
communities from regional species pools. Habitat choice and habitat specialization have
predictable consequences for community structure (Morris 1996, Cavender-Bares et al.
2009). For example, piscivorous organisms live near water, but they could be
specialized on different bodies of water (lake, river, small streams). We examine
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taxonomic β diversity (variation of species identities between sites) and
phylobetadiversity (variation in phylogenetic relatedness of communities between sites)
in three terrestrial ecoregions within the lowland tropical moist forest biome of Northern
South America, which harbors a large proportion of Earth’s terrestrial biodiversity (Olson
et al. 2001). Biodiversity is usually plotted as taxonomic richness of a geographic area
described by a certain index in reference to species numbers. Phylogenetic diversity
measures include additional information on phylogenetic relationships among species.
Using phylogenies can serve as a tool to generate hypotheses about processes that
lead to the assemblages that we observe.
Our study system is well known for high α diversity in single localities (Terborgh
et al. 1990). Environmental gradients (Pianka 1966, Brown et al. 2001, Tuomisto 2006,
Jankowski et al. 2009, Chase and Myers 2011, Kraft et al. 2011, Jetz and Fine 2012)
certainly play a role in the distribution of organisms; however, it is not clear how the little
variation in abiotic factors such as temperature, precipitation, or elevation affect
phylogenetic diversity and speciation in lowland tropical moist forests biomes such as
the Amazon basin. Climatically the Amazon basin is relatively uniform with few strong
abiotic gradients and there is little variation in elevation (<400m), annual mean
temperature, and precipitation (>2000mm) based on WorldClim v.1.4 data; yet, the
region is home to the most diverse terrestrial ecoregions on earth (Hijmans et al. 2005).
Amazonia contains 7 million square kilometers of land (Malhado et al. 2013), and
the formation of the current Amazon basin spans close to 60 million years with a
complex geologic history that is responsible for the isolation and connectivity of
replicated sets of habitats and communities (Hoorn et al. 2010). During this time major
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contributors to the formation of the basin were rivers that have changed their course,
the Andean uplift, formation of new rivers and wetlands, climatic changes, and global
sea level drops during the glacial periods (Hoorn et al. 2010). The area known today as
Amazonia was once part of a much larger “pan-Amazonian” region, which included the
area of the present Amazon, Orinoco, and Magdalena drainage basins. A diverse fauna
existed in this region, elements of which are now restricted to the Amazon basin. The
three ecoregions in our study - Napo Moist Forest (NAPO), Southwestern Amazonian
Moist Forest (SW) and Guianan Moist Forest (GUI) – include large expanses of
relatively undisturbed forest, and they all share the dynamic geologic history of the
formation the Amazon basin. Based on the large regional species pool, diversity of
habitats, heterogeneity of the landscapes and the relatively low variation of abiotic
factors, we can use the ecoregions of lowland tropical forest as a replicated study
system to evaluate the effects of habitat specialization on ecological and phylogenetic
dissimilarities of local communities.
We centered our investigation on four biological processes that are known to
affect community assembly: drift, speciation, dispersal, and selection. Our null
hypothesis is that the turnover of species between or within the local communities is not
different from random, which indicates that drift is responsible for species distributions.
Under this hypothesis we would predict that betadiversity and phylobetadiversity within
and between ecoregions and habitats would not be significantly different from random
sampling of the species pool. We test for an influence of deterministic processes by
comparing observed phylogenetic turnover between assemblages to a stochastic
expectation. Significant deviations from the stochastic expectation indicate that
25
deterministic processes more strongly influence community composition. We use drift
here synonymously with stochastic processes (Orrock and Watling 2010, Chase and
Myers 2011). We then test for evidence of several deterministic processes underlying
community organization. First, the speciation hypothesis is based on a strong influence
of local diversification processes. Local speciation can have profound effects on species
richness (Emerson and Kolm 2005), betadiversity, and phylobetadiversity (Emerson et
al. 2011) and is quite plausible given the extended periods during which various
sections of the Amazon have been isolated from each other, mainly by rivers (Hoorn et
al. 2010). If local diversification dominates community structure and organization, then
we would predict: (a) high betadiversity (few shared species) between habitats within an
ecoregion and among ecoregions; and (b) low phylobetadiversity within ecoregions and
(c) random patterns of phylobetadiversity among ecoregions.
The second hypothesized deterministic mechanism is dispersal, which is based
on the effects of the movement of organisms across space and their influence on
species richness and turnover patterns (Donoghue 2008, Wang et al. 2013, Peixoto et
al. 2014). This process, which is analogous to the processes of gene flow, hypothesizes
that patterns of community assembly are determined by limitations in dispersal rather
than selection imposed by the different habitats themselves. Under this scenario,
distance, connectivity, and species-specific dispersal abilities would be the main factors
underlying species turnover. Specifically, if dispersal processes dominate community
composition, then we would predict high species turnover between habitats that are not
connected within an ecoregion, but low betadiversity between habitats that are
connected within and between ecoregions. For example, riverine habitats have high
26
connectivity across large distances; local communities in river edge habitat, river
habitat, or along streams, therefore, should have lower turnover than expected by
chance. In contrast, isolated habitats such as white sand forests, bamboo patches or
inselbergs should be more different than any of the habitats we use for comparisons,
and terra firme forests that are isolated by rivers should also have higher species
turnover. Thus, the dispersal hypothesis predicts: (a) low phylobetadiversity among
habitats within an ecoregion, (b) low phylobetadiversity between habitats that occur in
multiple ecoregions, and (c) high phylobetadiversity in isolated habitats in all
ecoregions.
Third, the environmental filtering effect is based on the hypothesis that selection
by abiotic and biotic processes causes deterministic fitness differences between taxa
within a habitat or ecoregion (Swenson et al. 2012). Within the biome in question in this
study, temperature, precipitation and elevation do not differ significantly, but edaphic
conditions, and habitat heterogeneity do create differences. If environmental filtering
dominates community organization, then we would predict high betadiversity between
habitats within an ecoregion and between ecoregions, high phylobetadiversity in
comparison of unique habitats within ecoregions (e. g., bamboo, white sands), and low
phylobetadiversity between habitats that occur in multiple ecoregions.
The three ecoregions in our study offer both unique and replicate habitats. These
ecoregions are large areas of relatively uniform climate that harbor characteristic sets of
species and ecological communities that were defined based on expert opinions (Olson
et al. 2001). Investigating patterns of species richness and phylobetadiversity in diverse
landscapes in the framework of evolutionary biology (Vellend 2010) can elucidate how
27
habitat choice contributes to species sorting from a regional pool into local communities
and thereby the origin of the communities.
Methods
Species Lists
We assembled ecoregion species lists using InfoNatura NatureServe bird
distribution data (Natureserve 2016). We used species range maps and overlapped
them with the delimitation of the Napo Moist Forest Ecoregion, the Southwestern
Amazonian Moist Forest Ecoregion and the Guianan Moist Forest Ecoregion to create
the regional species pool lists. We ensured that the elevational ranges were
incorporated in the dataset, because range maps often do not include that information.
Marginally overlapping species were also removed, because shapefiles of ranges of
extralimital species sometimes overlapped with the boundaries of a specific ecoregion,
thereby artificially inflating the regional species pool.
To generate the habitat level classification, we selected three sites: Allpahuayo
Mishana National Reserve in the Napo Ecoregion, Cocha Cashu Biological Station in
Southwestern Amazonian Moist Forest Ecoregion, and Nouragues Biological Station, in
the Guianan Moist Forest Ecoregion. The bird communities of these three sites have
been intensively studied, which allowed us to generate appropriate habitat-level
classifications within the regional species pools. We used a combination of published
checklists (Robinson and Terborgh 1990, Thiollay 1994, Alvarez Alonso et al. 2012) and
data from detailed field observations and habitat descriptions from field guides (Stotz
1996, Schulenberg et al. 2007) to assemble the species lists for the three regional
species pools and the habitat pools. We assembled species lists for a total of 33 habitat
based local communities, and we removed vagrants (species that were only recorded
28
as very infrequently observed, wandering individuals that had no known local
populations within the regions) from species lists.
Species Composition Comparisons
We first compared the species composition of communities using the Bray-Curtis
dissimilarity (BCD; (Bray and Curtis 1957)). The BCD is the sum of the absolute value of
the differences in the number of species in two communities divided by the sum of the
total number of species at the two sites. A BCD of 0 indicates that two communities
have the same species composition, whereas a BCD of 1 indicates that the sites share
no species. We calculated the BCD for all pairs of communities based solely on species
composition, not abundance. This is equivalent to calculating BCD when the abundance
of each species in each community is either 1 (present) or 0 (absent).
Differences in richness among localities can bias comparisons using raw BCD
values. For example, when comparing a community with many species to a community
with few species, the BCD will be large simply due to the difference in the number of
species, even if the smaller community represents a subset of the larger community. To
account for this potential bias, we estimated the BCD that we would expect with the
same sampling, if all of the localities represented a single, homogenous community.
This was done by randomly permuting the species observation data to generate 10,000
data sets representing the distribution of species under the null model of a single,
homogeneous community. We permuted the list of species observed at each of the
localities using a random reshuffling technique that preserves the total number of
species observed in each locality. A single reshuffling move consists of randomly
picking two localities, and then randomly choosing a species that occurs at each
locality. If the species from the first locality does not occur in the second and the
29
species in the second locality does not occur in the first, we swap the locations of the
two species. To generate each randomly permutated data set, we performed this
reshuffling 10,000,000 times. The calculation of the BCD distances and the creation of
the randomly permuted data sets were done using Perl scripts written by J Gordon
Burleigh and followed the same methods as described in (Oswald et al. 2016)
For each pairwise comparison of localities, we divided the observed BCD
(calculated from the species lists) by the expected BCD (the average BCD calculated
from the 10,000 randomly permuted replicates). This ratio of observed to expected BCD
accounts for the differences in size and sampling effort among localities. If the ratio is
<1 or >1, the composition of the two localities is either more similar or more different,
respectively, than would be expected if all localities represented a single, homogenous
community. The BCDs calculated from the 10,000 random replicates also provide a null
distribution to test whether any two localities are more similar or different than expected
by chance.
Phylogenetic Composition Comparisons
Two localities may share few or no species and yet have a very similar
phylogenetic composition (e.g.(Graham and Fine 2008). To evaluate the phylogenetic
distance between all pairs of localities (i.e., the phylobetadiversity), we used the UniFrac
measure (Lozupone et al. 2011), which estimates the unique fraction of the phylogeny
represented by each locality. Given a phylogenetic tree that contains only species from
two localities, the UniFrac distance is the percentage of branch lengths in a
phylogenetic tree that are unique to a single locality. For example, if Locality A was
composed of species that formed a clade that was sister to a different clade composed
of the species in Locality B, the two localities would have a UniFrac distance of 1
30
because there is no shared phylogenetic history between the species in the two
localities. In contrast, if all species in Community A had closely related sister species in
Community B and all the species in Community B had closely related sister species in
Community A, the UniFrac distance would be small (close to 0), even if the localities
share no species.
Estimates of phylobetadiversity depend on a phylogenetic tree, usually with
ultrametric branch lengths. We pruned the avian phylogenetic tree of (Burleigh et al.
2015) to include only the 764 species from the detailed species lists. The tree contained
641 (83.9%) of these species. This tree and 100 bootstrap trees were built from a 29-
locus supermatrix using maximum likelihood (ML), implemented in RAxML (Stamatakis
2006),and the branch lengths were made ultrametric using penalized likelihood (with a
smoothing parameter of 100) implemented in r8s (Sanderson 2003). We first estimated
the Unifrac distances between all localities based only on the species ML tree. To
examine the possible effects of topology and branch length uncertainty, we also
estimated the Unifrac distances using all 100 ML bootstrap trees. To account for the
123 missing species, we generated 100 new trees by randomly inserting the missing
taxa into the ML tree in a position from the stem branch preceding the most recent
common ancestor of the species from the same genus, or if missing, the same family, in
the tree to the tips descending from the stem.
Like the BCD, the size of a locality or the sampling effort can greatly affect the
raw Unifrac distances. Therefore, for each comparison between two localities, we also
calculated the Unifrac distance using 1,000 randomly permuted data sets, created using
the same methods described for the BCD analyses. For each comparison between
31
localities, we divided the observed Unifrac distance by the average Unifrac distance
from the 1,000 random replicates, and this ratio was used as the pairwise distance
measure between localities.
Statistical analysis were performed using the R version 3.1.0 (R Development
Core Team 2016). Mantel tests were performed to measure the association between
the elements of two matrices, by taking into account the autocorrelation that exists
between the elements of each matrix (Mantel 1967) were performed using the ade4
package (Dray and Dufour 2007). This test is often used to test for a significant
association between different distance matrices. For network analyses the package
hclust and library igraph (Csardi and Nepusz 2006) we used, applying various linkage
methods to find the appropriate number of groupings.
Results
Species richness
The analyses revealed high regional species richness and variable local species
richness in different habitats. Overall, 1486 species were included in the three
ecoregions associated with the habitat types in the 33 assemblages compiled in this
study, 1194 species in Southwest Amazonia, 1072 species in the Napo Ecoregion, and
606 species in the Guiana Moist Forest. These high totals include some Andean foothill
species as well that have partially overlapping distributional maps, these were not used
in the final analyses. The AMNR dataset included 496 species, 473 at Cocha Cashu
and 420 species in Nouragues. The average number of bird species per habitat was 76
(range: 13 to 235). Primary forested habitats had the highest species richness.
32
Betadiversity
Of the 528 pairwise comparisons of Bray-Curtis dissimilarities of species
composition, 100 pairs (18.9%) of local communities showed results that were more
similar to each other than expected by chance (Fig. 2-2). Of these 100 pairs, 96
remained in the results when repeating the analysis with the 641 species that were in
the phylogenetic tree. All of these habitats were also more similar than expected by
chance to at least one other habitat, forming one large cluster with various
interconnecting links. There were 134 (25.4%) pairwise combinations that were
significantly different based on taxonomic betadiversity.
The hierarchical cluster analysis of the set of Bray-Curtis Dissimilarities identified
two large clusters with all agglomeration algorithms and grouping methods (Fig.2-3).
The forest habitats and the non-forest habitats form the two distinct clusters within
which the different habitats are arranged in similar dendrograms produced by ‘average’,
‘complete’, ‘single’, and ‘ward.D’ groupings (Fig. 2-3). The cluster of forested habitats
included local communities from all three ecoregions.
Phylobetadiversity
Of the 528 pairwise comparisons of phylobetadiversity of the 33 different habitats
(Table 2-2), 30 pairs (5.7%) of local communities (as defined by habitat preferences)
were statistically more similar to each other than expected by chance (Fig.2-4). Of these
30, 23 were also significantly more similar in the bootstrap replicates (adding a new
pair: h7-h18: Napo lakes and SW rivers), and in the maximum likelihood tree that also
included all species. Running the analysis through the bootstrap replicates and the all-
species tree also showed that secondary forest habitat from Guiana (h27) was similar to
33
(forested habitats from the Napo ecoregion (h3, h4, h5). High phylobetadiversity was
found in 227 pairwise comparisons (43% of all comparisons).
The local community comprised of habitat generalists (h1, any species that is
equally likely to occur in four or more habitat types) was phylogenetically more similar to
the community of birds mostly observed above canopy (overhead) (h26) in the SW, and
the open habitat of large openings (h32) in Guiana, represented relatively open habitats
from all three ecoregions. When comparing forested habitats, Napo secondary forests,
forest gaps, and primary forest edges (h3) were more similar to periodically inundated
forest in the same ecoregion (h5) and the periodically inundated forest (h14) and the
zabolo, canebrakes, and vines (h23) in the SW Amazonian ecoregion. The Napo
primary, terra firme forest (h4) was found to be phylogenetically more similar to the
periodically inundated forests in the same ecoregion (h5), and both of these mature
forest types were more phylogenetically similar to 4 other mature forest habitats: the
white sand forests in Napo (h12), the high ground forest (h13) and the periodically
inundated forest (h14) in the SW, as well as the Guianan mature primary forest (h28).
White sand forests (h12) were more similar to SW Amazonian high ground forest (h13),
periodically inundated forests (h14) and the latter two were also more similar to each
other, and to the Guianan mature forest (h28) as well.
Lakes and rivers of the Napo ecoregion were phylogenetically more similar to
each other, and the Napo river habitat was also linked to SW Amazonian river and lake
habitats (h7, h11, h18, h20 clustered together). Similarly, Napo lake edge and river
edge were both similar to SW Amazonian lake edge habitat, which was similar to the
river edge of the same ecoregion (h8, h10, h19, h21). The two aguajales communities
34
(birds living in Mauritia palm stands in broad, marshy clearings) also grouped together
with low phylogenetic turnover (h2 and h25). SW Amazonian swamp forest (h15)
clustered with forest stream margins (h16) of the same ecoregion. The SW forest
openings from treefall gaps (h17) clustered with Guianan disturbed or secondary forest
(h27), and the SW bamboo habitat (h24) was more similar to the Guianan treefall gaps
edges (h29).
Within ecoregions there were 18 pairs of communities with low betadiversity in
the NAPO, 19 within the SW and 3 within GUI ecoregions (27%, 21% and 14% of
comparisons, respectively). There was a higher number of pairwise comparisons with
high taxonomic turnover among these habitats: 26 in NAPO, 18 in SW and 8 within GUI;
39%, 20%, and 36% respectively (Fig.2-5a). Similarly, there were very few communities
that were phylogenetically more similar to each other than expected by chance: 5 within
NAPO (8%), 5 within SW (5.5%), none within GUI, and more communities had higher
phylobetadiversity than expected by chance: 30 within NAPO (45%), 49 within SW
(54%), and 4 within GUI (18%), respectively (Fig.2-5b).
The distance matrices based on Unifrac distances between local communities of
all species, only species in the phylogenetic tree, and the bootstrapped tree were all
significantly correlated with the BCD matrix based on species composition (Mantel test
results: all species, r=0.60, P<0.001; only in tree species r=0.68, P<0.001, bootstraps
r=0.57, P<0.001). All three sets of Unifrac distances were also significantly correlated
(Mantel test results: all species to in tree only, r=0.98, P<0.001; only in tree species to
bootstraps r=0.84, P<0.001, bootstraps to all species r=0.85, P<0.001). The relationship
between species richness, betadiversity and phylobetadiversity (Fig. 2-6) showed a
35
stronger correlation between species richness and phylobetadiversity results (lm,
p<0.01, R2 = 0.14).
Discussion
Our results demonstrate the role of beta diversity in maintaining the high regional
diversity of Amazonia. Using an approach that combined different spatial scales in three
different regions with phylogenetic comparison, we documented high betadiversity and
high phylobetadiversity in our pairwise comparison within ecoregions, and also among
regions. Yet, we also documented that a great many habitats that showed little
betadiversity or phylobetadiversity. Taken together, these results suggest that none of
our hypothesized processes underlying avian biodiversity in the Amazon can explain all
or even the majority of the patterns we documented. Instead, we argue that context-
dependent deterministic processes shape community assemblages in different habitats
with different levels of connectivity. Overall, we found strong evidence that species were
not simply randomly distributed; a large proportion of communities were either more
similar or more different than expected by chance, and the results did not support the
drift hypothesis that species were simply randomly distributed across habitats.
The results indicate that although many habitats shared species, only ~20% of
habitat comparisons had low betadiversity (were significantly more similar than
expected by chance) across ecoregions. Within the two western Amazonian ecoregions
there were more pairs of local communities that were significantly similar to each other
than expected by chance, but not in the Guianan ecoregion. These differences may be
explained by the broader distribution of species in the western Amazonian region, which
appears to contain species that are more likely to share habitats than they are in the
Guianan region. The Napo and SW Amazonian ecoregions harbor diverse local
36
communities that share more species with each other than with the Guianan moist
forest ecoregion, which could be explained by their proximity and shared
biogeographical histories.
Some of the patterns uncovered in our study are driven by widespread species
that occur across the region (Graham and Fine 2008), making species composition
between sites similar (low betadiversity and low phylobetadiversity). For example, the
similar patterns discovered in riverine communities and lakes can be explained by the
presence of species with wide distributions. In addition, riverine habitats are intrinsically
connected (Remsen and Parker 1983, Puhakka et al. 1992, dos Anjos et al. 2007,
Gillies and Clair 2008, Adams and Burg 2015); therefore, dispersal limitation is less
likely to lead to differences in communities. It is interesting to note that species living in
forested habitats, where dispersal limitation has been hypothesized as an important
mechanism, also showed less turnover than expected by chance. This can be explained
by habitat filtering or by strong biotic interactions between species, which we did not
test.
Taxonomic and phylogenetic betadiversity and bird species composition in
forested habitats were all more similar to each other than expected by chance,
regardless of whether they were in the same or different ecoregions. These
assemblages were in similar environments, but many were separated by large
distances. These results suggest that forest communities throughout the lowland
tropical moist forest biome have similar bird communities as shown by a comparison of
the 100-ha census results of Terborgh et al. (1990) and Thiollay (1988, 1994), which
generally only differ in details of species composition. Even with all of the complex
37
changes in connectivity through time, the communities of forests have diverged mainly
in species composition rather than in deeper phylogenetic composition, and even
forests with different levels of disturbance (floodplain versus terra firme) still are very
similar overall in phylo- and betadiversity. These results suggest that niche processes
structure these communities, which strongly supports the deterministic hypotheses and
suggest that drift and dispersal limitation are not strong factors affecting community
structure in forested habitats. It is also possible that the reduced phylogenetic turnover
among habitats is a direct result of the high species diversity of these habitats.
We found three larger clusters of habitat types ( forested habitats, open water,
and water edge) that form distinct clusters, and a few other identical habitat types that
form small clusters across ecoregions. Bird communities that share water edge habitats
such as lake edges and river edges in Western Amazonia also showed low
phylogenetic turnover, and lake and river communities also formed a cluster based on
low phylogenetic betadiversity. The bird communities living in the aguajales, dominated
by Mauritia flexuosa palms, also clustered together, as they share a large number of
habitat specialist species, and these habitats are also connected because they occur
along major river floodplains. Aguajales occur more in Western Amazonia, and were
missing from our GUI dataset. These patterns were only revealed using the
phylogenetic betadiversity metrics. These clusters could also be explained by strong
effects of biotic, niche-based processes determined by elements of vegetation for the
bird assemblages of the communities in these particular habitats (Emerson and
Gillespie 2008, Graham and Fine 2008, Kraft et al. 2014, Weinstein et al. 2014).
38
Habitats that form along linear habitat features such as rivers are connected
across large distances, and in Amazonia in general terra firme forest are the dominant
forest type that comprises the matrix in which other habitats are embedded. However,
there are certain Amazonian habitats that form on essentially permanent soil formations
such as WSFs and consist of small, isolated patches in a matrix of terra firme, which
can create a potential barrier to gene flow. Despite their island-like configuration
(Prance 1996) and the number of habitat specialist species that do not occur in other
habitats (Alvarez Alonso 2002, Alvarez Alonso and Whitney 2003, Alvarez Alonso et al.
2013), the bird communities of white sand forests were also found to be similar to other
forested habitat types. This result suggests that despite the differences in floristic
composition (Tuomisto et al. 2003, Fine et al. 2010, Pomara et al. 2012), there is little
evidence for a unique bird community in WSFs other than the relatively small proportion
(less than 10%) of specialists. Local communities varied in species richness, and as
species richness increased, phylogenetic turnover decreased among communities;
richer communities were more similar to each other, although there was no strong
congruence between PBD and species richness. This could also explain why all the
forest habitats seem to be more similar to each other, because these communities have
the highest number of species.
When combining all three ecoregions in the analysis, over a quarter of the
communities were more different from each other across ecoregions than expected by
chance, and the differences among local communities within ecoregions in terms of
taxonomic diversity were even higher. The results of sets of communities that showed
both high phylogenetic betadiversity and high betadiversity can be explained by the
39
presence of a high proportion of small-range species, at least some of which come from
more distantly related lineages. Close to two thirds of these comparisons, however,
were based on pairs of habitats that contrasted a forested habitat type with an “aquatic”
habitat type. Bird communities in those habitats rarely share species; therefore, the
strong patterns of turnover can be explained by the intrinsic differences of the habitats
themselves.
Our results indicate that local communities were both taxonomically and
phylogenetically more distinct than expected by chance both within and among
ecoregions, not an unexpected result in such a highly diverse region. We did not find
that habitats within ecoregions formed clusters. In fact, habitats from distant ecoregions
were more similar than expected by chance, and only very few (0-8%) pairwise
comparisons were more similar to each other within ecoregions. Therefore, we found no
support for the speciation hypothesis, which predicted that the greatest differences
would occur among regions in which geographic isolation had led to speciation, and
within ecoregions it predicted low phylogenetic turnover.
Our plot data can be considered complete. There are few complete datasets from
the Neotropics due to the high diversity and the expertise needed to census these
areas; however, the number of well-studied sites is growing. Our study includes species
lists assembled from sites distributed over a vast geographic area, and from sites
separated by great distances (more than 900 km between Napo and SW, more than
2300 km between Napo and French Guiana, and more than 2500 km between SW and
French Guiana). Distribution ranges of most bird species in the analyses do not span
40
these distances, and dispersal distances for individual forest species is known to be
limited (Blake 2007, Moore et al. 2008).
In conclusion, we found strong evidence that communities have deterministic
assembly rules within the Amazon Basin, and results of comparisons are context-
dependent. The relative roles of dispersal and niche processes differ in specific kinds of
habitats that vary strongly in resources and habitat structure (e. g., aquatic and forest
habitats), in connectivity (riverine versus more isolated patches of permanent soil
formations), and in habitats with generally similar structure and resource availability (e.
g., the overall similarity in forests of all ecoregions). Our phylogenetic analyses allowed
us to disentangle turnover due to replacements of closely related species in regions
isolated by rivers from environmental filtering in habitats in which fundamentally different
processes shape bird communities (e. g., aquatic versus terrestrial habitats).
Phylogenies therefore served as a tool to generate further hypotheses about processes,
even if they do not necessarily allow us to test processes directly (Mayfield & Levine
2010).
To elucidate the proximate mechanisms of habitat filtering, we would like to
investigate what traits are shared among species in habitats that show clustering and
low phylogenetic turnover across distant regions. We also need to investigate if other
taxa show similar patterns to birds, and have already started collecting species list
information for plant communities. Strong shared patterns across multiple groups or in
the same geographic region could help us identify centers of endemism (Gonzalez-
Orozco et al. 2015).With the fast-growing publication of detailed phylogenetic trees and
advanced computational approaches, it will be possible to continue investigating this
41
question, explore whether there is turnover across entire lineages, and how the size of
species ranges affect patterns of turnover..
42
Table 2-1. Ecoregions and habitat types used in this study
Habitat NAPO SW GUIANA
Generalist (more than 4 habitats) h1 h26
primary terra firme forest h4 h13 h28
secondary forest h3 h27
inundated forest h5 h14 h33
white sand forest h12
agricultural/disturbed area h6 h32
aguajal h2 h25
forest opening h17 h29
bamboo h24
zabolo h23
marsh h22
swamp h15
bare rock h30
lake h7 h20
lake edge h8 h21
river h11 h18 h31
river edge h10 h19
forested creek edge h9 h16
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Table 2-2. Summary of predictions of the four hypotheses tested.
Hypothesis Betadiversity Prediction Phylobetadiversity Prediction
Null (drift) Random Random Speciation/Biogeography (historical isolation)
High within ecoregions High between ecoregions
Low/random within ecoregions High between ecoregions
Dispersal
Low within ecoregion Low between ecoregions Low between habitats
Low within ecoregion Low between ecoregions Low between habitats
Environmental filtering
High within ecoregions High between ecoregions High/Low between habitats context dependent
Low within ecoregions High/Low between ecoregions context dependent
44
Figure 2-1. Map of the three ecoregions used for assembling the regional communities.
The labeled and outlined ecoregions all belong to the moist tropical forest biome.
45
Figure 2-2. Network representation of the cluster of communities that are taxonomically
more similar to each other than expected by chance. The color of the spheres represents the ecoregion (red=NAPO, blue=SW, black=GUI).
46
Figure 2-3. Cluster dendrograms using taxonomic betadiversity and phylobetadiversity
for all local bird communities in different habitats across the three ecoregions, using average, complete, single and ward.D linkage methods. Labels correspond to habitat codes from Table 2-1.
47
Figure 2-4. Clusters of communities in habitats that are phylogenetically more similar to
each other than expected by chance. This network representation of all the phylobetadiversity comparison of the 33 habitats in 3 ecoregions. The color of the spheres represents the ecoregion (red=NAPO, blue=SW, black=GUI). Links represent two habitats that were phylogenetically more similar to each other than expected by chance.
48
A)
B)
Figure 2-5. Within-ecoregion betadiversity phylogenetic betadiversity. Only statistically
significant pairwise comparisons within each ecoregion are shown. A) Betadiversity results using taxonomic turnover. B) Phylogenetic betadiversity results within each ecoregion. Black linkages represent pairs of communities more similar to each other than expected by chance. Red linkages represent pairs of communities more different from each other than expected by chance.
49
Figure 2-6. Relationship between phylogenetic betadiversity (PBD, black points)
betadiversity (BC, blue points), and species richness. The lines represent best fit lines corresponding to linear regressions.
50
CHAPTER 3 STRUCTURE OF UNDERSTORY BIRDS COMMUNITIES IN FORESTS ON TWO
CONTRASTING SOIL TYPES IN AMAZONIAN PERU
Introduction
Amazonia hosts the highest terrestrial forest diversity in the world in spite of its
low climatic and topographic variability. One hypothesized mechanism for the
maintenance of this diversity is the extraordinary range of different habitat types present
in the region ((Rosenzweig 1987, 1995, Borges 2013, Arellano et al. 2014, Castano-
Villa et al. 2014), and Chapter 2). These habitat types may be sufficiently different in
resources and structure that they support different bird communities, including many
habitat specialists. Habitat differences may further enhance diversity by allowing closely
related species to coexist through habitat segregation, which can be either passive or
active (Robinson and Terborgh 1995, Nores 1999).
Extensive geological and botanical studies show that the soils in Western
Amazonia are composed of a mosaic of geologically different substrates with different
chemical properties that influence the forest flora (Ruokolainen et al. 1997, Tuomisto et
al. 2002, Ruokolainen et al. 2005, Ruokolainen et al. 2007, Asner et al. 2014, Higgins et
al. 2014). The avifauna also co-varies with both floristic composition and the soil
nutrients in upland forests (Pomara et al. 2012). One of the most distinctive soil types is
on white sand soils, which are associated with plant communities that differ from those
on the more dominant clay soil. Five types of white sand forests are generally
recognized in Peru, each of which differs in structure and physiognomy,: high-dry-open
(varillal alto seco, tall dry varillal), high-wet-open (varillal alto húmedo, tall humid
varillal), low-dry-dense (varillal bajo seco, low dry varillal), low-wet-open (varillal bajo
húmedo, , low humid varillal) and low, dense forest (chamizal) (García Villacorta et al.
51
2003, Garcia-Villacorta and Hammel 2004, Coronado et al. 2015). For the purpose of
this study we reduced the five WSF categories to three, humid and dry varillales and
chamizal. Each of these white sand forest types differs from the others but all share
characteristics that distinguish them from forests on clay soils. Vegetation changes
among the habitat types both in composition (Fine 2010) and structure (Jirka et al.
2007, Ferreira et al. 2010). White sand soil forests contain many habitat specialist
plants that tend to be slow-growing and short, traits associated with the lower
productivity of sandy soils, which do not hold water well and are often leached of their
nutrients (Fine et al. 2005, Fine et al. 2006). The trees in most white sand forests are
also more slender, shorter, and they often have very hard, sclerophyllous leaves. The
understory is relatively sparse in WSF, with fewer herbs except for terrestrial ferns,
which are very common - especially Trichomanes spp., Elaphoglossum spp. and
Lindsaea divaricata (Gentry 1988). The humid varillales have relatively poor drainage in
comparison with other WSF, not much organic matter, exposed roots covering the
ground and low tree species diversity. Caraipa utilis (Clusiaceae) is often dominant in
these forests. Dry varillales have better drainage, little organic matter and many more
tree species. Chamizal vegetation is shorter, wet, usually 3-5 m high, with dispersed
emergent trees up to 8-12 m tall. It is dominated by shrubs such as Graffenrieda limbata
(Melastomataceae), and interspersed throughout with small palms, e.g. Mauritia and
Mauritiella, and only a few species of ferns (Gentry 1981, García Villacorta et al. 2003).
Given these differences between white sand and clay soil forests and among the
different kinds of white sand forests, it is likely that the bird communities associated with
these different habitats will also differ in composition, trophic and guild structure and
52
abundance. We documented in Chapter 2 that forested habitats harbor unique,
taxonomically distinct communities, some with specialists, and comparisons of the
community structure may shed additional light on the selective environments underlying
the role of these white sands plant communities in maintaining the high beta diversity of
Amazonia. In this study we used mist nets to sample adjacent communities on different
kinds of white sands forest and compared them with the clay soil matrix terra firme in
which the white sands patches are embedded in the AMNR. We compared bird species
richness, capture rates, turnover (species replacement) among communities, and
nestedness (differences due to species loss or addition among different sites) among
white sands forests and with forests growing on clay soils.
Our first hypothesis is that habitat productivity is the primary determinant of the
overall abundance and diversity of birds in communities. A simple prediction of this
hypothesis is that clay soil forests, which are generally more productive (Higgins et al.
2014) than white sands forests would have the highest abundance and richness of
species, followed by humid varillal, dry varillal, and chamizal in order of decreasing soil
nutrient content and vegetation height. According to this hypothesis, the richness of
most of the trophic guilds should vary in parallel with fewer species and captures in all
guilds, reflecting the overall reduced availability of resources in all trophic levels due to
lower productivity. Alternatively, the lower stature of the forest in white sands may lead
to higher capture rates because the mistnets used to capture birds are more likely to
sample the entire community rather than just the birds living in the understory (Remsen
Jr and Good 1996). In this case, the capture rate of canopy-living species should
increase with decreasing vertical habitat structure.
53
An alternative to the productivity hypothesis is that community composition will
more closely reflect idiosyncratic differences in vegetation structure and the resources
available in the different habitats in relation to the details of plant community
composition and structure (Holmes et al. 1979, Robinson and Holmes 1982, Bush and
Holmes 1983, Holmes and Recher 1986, Kornan et al. 2013) or the associated fruit and
insect resources that are available to birds (Kissling et al. 2007). We predicted that
guilds will vary independently with local, habitat-specific vegetation structure and
resources available. We first hypothesized that the composition of frugivorous birds,
which are captured readily in understory mistnets (Karr et al. 1991, Loiselle and Blake
1999, Wunderle et al. 2005, Kissling et al. 2007, Blake and Loiselle 2009), will change
with the composition of the forest (Hasui et al. 2007, Vidal et al. 2014). Specifically, we
predicted that: (a) there will be relatively higher capture rates and a higher proportion of
frugivores in the understory of WSF reflecting the dominance of fleshy fruited
Melastomes, a bird-dispersed fruit; and (b) based on the dominance of Melastomes, we
further predicted that frugivorous species known to prefer Melastomes (e.g., Mionectes
flycatcher and manakins) would be proportionately more abundant and caught at higher
rates in WSF.
Second, we predicted that white sands forests will also contain a higher overall
proportion and capture rate of ant followers reflecting the general dominance of
hymenopterans (Saaksjarvi et al. 2004, Fine et al. 2006, Saaksjarvi et al. 2006, Gomez
et al. 2015) in the drier white sands soils relative to other groups of insects, which tend
to be less abundant on white sands soils (Vormisto et al. 2000, Saaksjarvi et al. 2006,
Fine 2010, Lamarre et al. 2012, Lamarre et al. 2016b). And third, we predicted that
54
multi-species flocking species and non-flocking foliage gleaning insectivores will be
caught at lower rates in white sands forests because of the low density of understory
foliage. Conversely, we predicted that sally gleaning insectivores, which tend to forage
in more open habitats, would be captured more often in the open understory of white
sand forests.
Methods
Study Region
Our study site was the Allpahuayo-Mishana National Reserve (AMNR) in the
Department of Loreto, northeastern Peru (latitude -3.922904, longitude -73.516645).
The study area is accessible from the Iquitos-Nauta highway, a paved road on the
southern border of the reserve, or the Nanay River along the northern border. Sampling
sites were established in closed-canopy white sand forest patches that were accessible
on foot (elevation: mean = 126.5m, range = 91.9 - 165.9m), and some sampling was
simultaneously completed in adjacent closed-canopy forests growing on alluvial terra
firme clay soils (elevation: mean = 116.7 m, range = 106.1 – 130.6 m).
Vegetation structure was measured at each patch and around each netline within
square 10 m by 10 m plots using a protocol adapted from the breeding biology research
and monitoring database field protocol (Martin et al. 1997)). Variables summarized from
vegetation structure data generally describe aspects of forest vertical structure or
understory vegetation density. Forest vertical structure variables included average
canopy height, number of trees greater than 10 cm dbh (tallied within a 20 by 20 m plot
surrounding each 10 by 10 m plot); and total tree basal area. Understory vegetation
variables included density of small stems (smaller than 2.5 cm dbh; less than 1 m in
55
height), density of large stems (greater than 2.5 cm dbh), and leaf litter depth
(summarized in Table 3-1).
Bird Surveys
We evaluated community composition of understory birds in an intensive mist-
netting and mark-recapture program from June 2009 through December 2012. We used
constant-effort mist-netting (Dunn and Ralph 2004, Nur et al. 2004) to survey understory
birds in the reserve. All nets were 38-mm mesh, 12 m x 3 m, tethered with five
trammels. We created transects through the forest interior using a line of 20 nets set on
existing foot trails in primary forest or older secondary growth forest contiguous with
primary forest. Nets were separated from one another by <1 m when possible. The
minimum distance between transect sites was 250 m. The greatest distance between
any two transects was 18 km. Each net transect was opened for ~500 net hours during
three consecutive days. We opened nets by 06:00 and checked nets approximately
every 30 min until 16:00 on the first two days and 11:00 on the third day. To reduce net
avoidance, we made sure that a site was not sampled for longer than 3 consecutive
days. We have limited data from open areas and second-growth as well. Through the
studies of Alvarez Alonzo, Juan Diaz and Noam Shany, a complete description of the
avifauna of this reserve is available (Alvarez Alonso et al. 2012).
Avian Guild Classification
All captured birds were identified, aged, sexed, and, if appropriate, marked with a
metal band, or by clipping the tip of a tail feather to recognize recaptures. Birds were
classified into feeding guilds using references on diets of tropical birds, and first-hand
observations of foraging in the field. Our classification also follows Terborgh et al (1990)
for species occurring in lowland tropical forest and a number of studies with diet
56
information compiled by Wilman et al. (Terborgh et al. 1990, Wilman et al. 2014).
Species were assigned to specific guilds based on foraging stratum, diet and substrate
according to the same references (Robinson and Terborgh 1990, Wilman et al. 2014).
We designated species as frugivores, nectarivores, granivores, omnivores, and
insectivores. Carnivorous species such as raptors, and piscivorous species such as
kingfishers were combined in our foraging guild analysis as they do not typically get
caught in mist nets and are undersampled (Remsen Jr and Good 1996). This guild
classification is based on the dominant trophic level for each species; we recognize that
many species simultaneously occupy more than one trophic guild. Even nectarivorous
hummingbirds supplement their diet with insects. Assignment of species that did not
have such ecological data available in the literature was characterized based on
personal observations.
Obligate, near obligate or facultative habitat specialization was assigned based
on the description of the bird community by Alvarez et al. (Alvarez Alonso 2002, Alvarez
Alonso and Whitney 2003, Alvarez Alonso et al. 2013). A guild of 17 species that
participate in mixed-species foraging flocks was also constructed; canopy species were
treated in a separate analysis. Sally-gleaning species that search more in the open and
fly relatively long distances to attack prey (Remsen and Robinson 1990) were also
distinguished from those that glean from nearby foliage (shrub gleaners that are not in
mixed species flocks). Ant followers and terrestrial insectivores were identified using
field observations, and ecological information from literature (Robinson and Terborgh
1990, Stotz et al. 1996, Schulenberg et al. 2007)
57
Data Summary and Analyses
Sampling effort differed between WSF (37000 net hours: 20500 in humid, 13500
in dry and 3000 in chamizal) and terra firme clay (9500 net hours). Therefore, we
compared captures and species richness across sampling events standardized to 500
net hours per event. We used raw species richness and rarefied species richness, and
they produced similar rankings. Our analyses are based on capture rate, excluding
same-day recaptures, which we calculated for each sampling period. Capture rate is
interpreted as a measure of bird activity in the understory (Karr 1979, 1981, Martin and
Karr 1986, Karr et al. 1991), which is potentially useful for comparisons over time and
among patches. We recognize biases with mistnet sampling (Remsen Jr and Good
1996) and will discuss their potential influence on some of our comparisons, especially
those dealing with habitats with very different canopy heights. All study sites were in
forest and had substantial undergrowth; therefore we assumed that habitat-specific
capture rates among guilds did not vary in such a way to alter the validity of the results.
Analyses were performed at the level of the sampling event (N=93, at 500 net hours
each), at the level of netlines (N= 42; hereafter “netline level”) and for particular
questions at the level of white sand forest patches (N=8 WSF patches; hereafter “patch
level”). We calculated percentages for each category from the number of captures, and
number of individuals. We also calculated species accumulation curves for the two
general habitat types (clay vs sand) and for the four specific habitat types (terra firme,
humid varillal, dry varillal and chamizal) to check for adequate sampling and rarefaction
analyses based on the minimum number of captures in the habitat type.
We examined patterns of species diversity (overall and within each guild) across
netlines and patches by the following metrics: total number of species, number of white
58
sand specialists, and number of rare species. We examined dissimilarity values
between white sand netlines and adjacent terra firme forest netlines to assess species
turnover and nestedness between contrasting habitat types. Measurements of change
in species composition across sites (e.g., beta diversity) that are expressed as
dissimilarities can reflect two separate processes: change due to species loss
(nestedness) and change due to species replacement (turnover) (Ruokolainen et al.
2005, Tuomisto 2010a, b, Baselga and Orme 2012, Baselga and Leprieur 2015). We
performed rarefaction analyses with standard samples for all species, and 95%
confidence intervals were calculated from rarefaction curves. The lack of overlap among
them was interpreted as significant differences in diversity among samples (Gotelli and
Colwell 2001, Gotelli and Entsminger 2001). We summarized pairwise bird dissimilarity
values of turnover and nestedness for each netline and patch. In case of replicates
across multiple years we also calculated averages of pairwise comparison across the
years. We also performed cluster analyses using the beta diversity index and
hierarchical clustering algorithm. Several grouping methods were used to visualize the
cluster dendrograms. Dissimilarity matrices for beta diversity were calculated in R v.
3.1.0 (The R Foundation for Statistical Computing 2015) using the vegan package.
We used a non-metric multidimensional scaling (NMDS) ordination to
characterize the compositional diversity of each habitat using capture data. The capture
data were standardized by the total number of captures in each site. Similarity matrices
were built using Bray–Curtis index. We applied a one-way analysis of similarity
(ANOSIM) to different similarity matrices to test the null hypothesis that the WSF and
clay habitats have no significant differences in species composition.
59
Results
Community-wide Patterns
During 22 months of banding, we recorded 8328 captures in forested habitats of
5057 marked individual birds. We captured 142 species with mist-nets (approximately
29% of the list of the area) in 46,500 net hours (Appendix A Table A-1.). The 142
species were from 114 genera and 32 families captured in the forest at least once
(including both clay and white sands). In white sands, 126 species were caught in 6421
capture events, and in clay-based forests 105 species were caught in 1907 captures.
The average capture rate for 500 net hours in WSF was 89.6 ± 29.2 SD individuals
compared with 100.4 ± 31.6 SD in clay soils. We recorded 1907 captures in clay during
19 sampling events, 2400 in humid varillal during 27 sampling events, 3621 in dry
varillal during 41 sampling events, and 400 captures in chamizal during 5 sampling
events. On average, overall capture rates were not significantly different between white
sand forest and clay forest netlines (Shapiro-Wilk normality test, Welch’s Two sample t-
test, p=0.099, Appendix A Fig. A-1A) or among the four habitat types overall (Shapiro-
Wilk normality test, ANOVA, p=0.092, Appendix A Fig. A-1B), although there was a
trend towards decreasing capture rates with decreasing productivity. The small sample
size of netlines in chamizal (N=6) limits the power of this analysis.
The average number of species caught per sampling event in WSF was 25.3 ±
5.8 SD. In the clay soil forests, the average number of species was 31.5 ± 8.8 per
sampling event. The number of species captured in the two general habitats differed
significantly (Welch’s Two sample t-test using log transformed species number data,
p=0.01043, Fig. 3-1). The number of species captured also differed among the four
60
different habitat types (log transformed species data passed the Shapiro-Wilk normality
test, ANOVA, p=0.0177).
The species captured in WSF and in terra firme clay also differed in their rank-
ordered distributions using proportion of captures in the particular habitat type (Fig. 3-2).
The most abundant bird in the sample of both habitats was the bark-foraging
Glyphorynchus spirurus, representing greater than 20% of captures in WSF. Whereas
Dixiphia pipra was commonly captured in both habitats (second most common in WSF
and fifth most common in clay), the second most abundant species in clay is a different
manakin species: Lepidothrix coronata. This particular manakin species was previously
reported as absent from WSF (Alvarez Alonso 2002), but we captured 58 individuals in
WSF suggesting that they regularly use this forest type.
Fifteen species that were captured in clay-based terra firme were never captured
in white sand forest, but each of these species represented less than 0.5% of clay forest
captures, and cumulatively made up only about 2% of the overall clay captures:
Automolus infuscatus, Bucco tamatia, Crypturellus soui, Electron platyrynchus,
Pteroglossus azara, Pygiptila stellaris, Ramphocaenus melanurus, Rhynchocyclus
olivaceus, Xiphorhynchus guttatus, Saltator grossus, Monasa morphoeus, Euphonia
xanthogaster, Myiobius barbatus, Thamnophilus schistaceus, Hypocnemis peruviana.
Given the much greater sampling effort in WSF, it is likely that these species are very
rare or absent in WSF.
In contrast, 36 species were captured in white sand forests that were never
captured in clay (Accipiter superciliosus, Ancistrops strigilatus, Campephilus rubricollis,
Cyanerpes caeruleus, Cyanerpes cyaneus, Hylocharis sapphirina, Micromonacha
61
lanceolata, Microrhopias quixensis, Oporornis agilis, Poecilotriccus latirostris,
Thamnophilus doliatus, Tityra semifasciata, Trogon rufus, Turdus lawrencii, Cacicus
cela, Caprimulgus nigrescens, Cercomacra nigrescens, Chrysuronia oenone,
Deconycura longicauda, Lophotriccus vitiosus, Neoctantes niger, Notarchus ordii,
Pachyramphus marginatus, Phoenicircus nigricollis, Celeus grammicus, Chlorostilbon
mellisugus, Claravis pretiosa, Philydor erythrocercum, Sclerurus caudatus, Phaeothlypis
fulvicauda, Rhytipterna simplex, Attila citriniventris, Hylocharis cyanea, Heterocercus
aurantiivertex, Cnemotriccus duidae, Neopelma chrysocephalum). Most noteworthy of
the above list is the species Neopelma chrysocephalum, which makes up ~3% of all
captures in white sands but was never present in clay forest. N. chrysocephalum is an
obligate white sand forest specialist, and is locally common in the habitat. These
apparent habitat specialists constituted ~4.7% of all captures in white sand forest, but
17 of these species typically forage in the canopy. Forest canopy is shorter in WSF (Fig.
3-3, ANOVA, p<0.05 for all comparisons), which may increase the odds of their being
captured in mist nets. Canopy birds that occasionally forage in the understory were also
more likely to be caught more with increasing netting effort. There were marginally more
canopy species captured in the shortest chamizal forest (17% in chamizal compared to
~13% in all other habitats), but there were no significant differences (Pearson’s chi-
squared test statistic=0.5031, p=0.9182).
Most species occurred only at a few sampling sites, and only relatively few
species were widely distributed enough to be caught in the majority of sampling events
(Fig. 3-4). The 20 most abundant species represented ~80% of captures in WSF and
~60% of captures in clay forest, a fairly typical distribution in avian communities in which
62
most species are rare or rarely encountered. These data further suggest an oligarchy of
a few very abundant species with many rare species (Robinson et al. 2000); 63 species
were captured on fewer than 5 sampling occasions and 29 species (20% of all species
sampled) were only recorded on a single netline.
Species accumulation curves based on rarefaction using all species varied
among specific habitat types (Fig.3-5), with clear separation of the three white sand
forest habitat types and the clay forest. The combined species accumulation curves
based on the 42 sampling sites do not show a clear asymptote (Appendix B Fig. B-1)
with any of the estimators (observed species richness, Chao, jackknife, or bootstrap)
comparing the specific habitats of terra firme clay and humid varillal, dry varillal and
chamizal (Appendix B Table B-1).
Compared to turnover, nestedness contributed much less to dissimilarity
between white sand and clay soil forest bird communities, and the two habitat types
show overlapping results in all dissimilarity measures (Fig. 3-6). Using Bray-Curtis
dissimilarity between WSF and terra firm clay for an ordination plot using netlines as
sites, we did not find clear separation between the communities, which overlapped
broadly (Fig. 3-7).
Vegetation Structure
Vegetation structure was similar in the white sand habitats, but different from the
terra firme forest (summarized in Table 3-1). Leaf litter depth was shallowest in clay soil
forest, and all white sand forest habitats had significantly deeper leaf litter than the terra
firme forest. Canopy was tallest in clay, followed by humid varillal, dry varillal, and
significantly shorter in chamizal than in any other habitat, although the only statistically
significant differences were in canopy height of chamizal compared to all others. The
63
average DBH of trees was significantly smaller is dry varillal and chamizal than in humid
varillal or terra firme clay, and the number of small stems (less than 2.5 cm DBH)
followed the same pattern, while the number of large stems was different in chamizal
only.
Avian Guilds
The dietary trophic guild structure of the samples was very similar among
habitats. Insectivores dominated most habitats proportionally (Table 3-2) and the
proportions of samples in different broad dietary categories did not differ significantly
when comparing clay to WSF (Pearson’s Chi-squared test, p=0.62) or among all four
habitat types (p=0.998).
We also compared the differences in capture rates and number of species per
habitat per guild in birds that are frequent members of mixed-species foraging flocks,
non-flocking shrub gleaners, army ant followers, terrestrial insectivores, dead-leaf
probers, sally-gleaners, non-canopy frugivores, and nectarivores using MANOVA
among the four habitat types (Table 3-3). We found significant differences in capture
rates among habitats for shrub-gleaning species, members of mixed species flocks (Fig.
3-8), and terrestrial insectivores (Fig. 3-9), and also marginal differences in nectarivores
(Fig. 3-10), but no differences in ant followers and sally-gleaners. The number of
species in these guilds was lower in chamizal for most categories, although the small
number of netlines in chamizal limited the power of these comparisons. We excluded
canopy birds from the comparisons, and rarefaction analysis (Fig. 3-11) for just the non-
canopy species showed similar asymptotic species accumulation curves as well as
differences between clay soil and the WSF habitats.
64
Discussion
We predicted reduced species richness and abundance with lower resource
availability in forests growing on less productive white sand soils, especially in chamizal
habitat, although with specific differences for certain groups such as an increase in
frugivores, and more army ant followers. As predicted, the bird communities on sandy,
poorer soils showed lower numbers of species and individuals than on richer clay soils,
confirming that productivity predicts species richness in the understory bird community
(Appendix A, Figure A-1A). The three white sand forest types did not vary significantly in
overall capture rates, although the trend was in the predicted direction; the low number
of samples in chamizal may have limited the power of this analysis (Appendix A, Figure
A-1B). We found partial support for our hypothesis that if resource availability and
habitat structure vary in contrasting habitats, then guild structure should also differ: the
proportions of dietary trophic guilds were the same in all four habitats, but some more
specific guilds based on foraging behavior differed significantly. Habitat heterogeneity is
one of the classical mechanisms promoting regional diversity, and the results of
analyses of species turnover in our system confirm that white sand forests add to the
regional diversity of the area by providing a different mix of niches available to birds,
some of which were habitat specialists.
In our surveys, we recorded 26% of the species classified as rare or absent from
white sands in our white sand sites. These results show the extent to which mistnet
samples potentially differ from song censuses. Even if some species do not actively
defend white sands forests vocally, they may use them for feeding and disperse through
them (see also Chapter 4).The examination of the between-year return rates in our data
(Appendix B Table B-2), however, do not support the hypothesis that WSFs act as
65
holding areas for transient or dispersing young birds. The return rates of birds from all
categories, including species previously characterized as species that avoid WSFs,
were the same as recapture rates for species considered to be residents of WSFs
(Alvarez Alonso and Whitney 2003, Álvarez Alonso et al. 2013).
We did not detect different trophic guild composition in bird communities living in
forests on different soil types, which suggests that there were few differences in the
overall availability of the major resource groups (fruit, nectar, insects) in the forest types.
Therefore, the declines in richness with soil productivity were probably not related to
major changes in the availability of food at different trophic levels (Kissling et al. 2007).
Rather, it appears that species loss was restricted to certain guilds defined by a
combination of foraging behavior and diet.
Both flocking and non-flocking species had higher capture rates in terra firme
clay, and had fewer species in chamizal habitat (14 species in clay, 11 in humid, 10 in
dry and 4 in chamizal), a possible reflection of the sparser foliage of the forest
understory in WSFs. Understory mixed-species flock species and non-flocking shrub
gleaners were significantly more common in terra firme clay forest types (Fig.3-8). The
loss of multi-species flocking species followed a nested distribution with the loss of the
sentinels and some of the foliage-gleaning insectivores as the soils became
progressively poorer. Chamizal forest, for example only retained two flocking species
compared with 7 in terra firme. The sentinel species, Thamnomanes, which depends
upon insects flushed by other species (Munn and Terborgh ) dropped out of flocks
entirely in chamizal, perhaps reflecting the lack of enough individuals of other species
available to flush insects. Epinecrophila haematonota,a dead-leaf gleaner, also dropped
66
out, but other frequent flock members such as Myrmotherula axillaris and
Xiphorhynchus elegans did not drop out of even the driest, most stunted forest. These
species were observed foraging apart from mixed-species flocks in these drier habitats.
The non-flocking shrub gleaners were caught less often, but there were also a number
of species replacements, including some of the WSF specialist species (Myrmeciza
castanea, Percnostola arenarum), suggesting the possibility of competitive exclusion.
Insectivores dominated all white sand and clay habitats.
Insectivores dominated all white sand and clay habitats. Within this dietary guild,
we expected that army ant followers would be overrepresented in WSF, especially
because we observed more frequent, larger ant swarms in this habitat, but there were
no differences in capture rates of army ant followers, although the number of species
dropped off in the chamizal habitat (Fig. 3-8A for capture rates and Fig. 3-8B for number
of species per guild per habitat). Ant-following insectivores are sensitive to the isolation
of forest fragments (Stouffer and Bierregaard 1995), but they did not differ in capture
rates. In general, the ant followers showed a tendency towards nestedness with the loss
of the larger obligate ant followers (fewer Phlegopsis and Myrmeciza fortis) in WSF with
declining soil productivity.
Given the greater thickness of the leaf litter in WSFs (Table 3-1), we might have
predicted higher capture rates of terrestrial insectivores in this habitat, but the pattern
actually showed the opposite trend. The thicker, sclerophyllous leaves of WSF and the
dry soil may slow the decay of the leaf litter, which may reduce the productivity of this
litter layer such that it is thicker, but less productive. Dead leaf probing gleaners had
approximately the same number of species in clay, humid and dry varillal, but fewer in
67
chamizal. Thryothorus coraya and Hyloctistes subulatus were not detected in chamizal,
but were captured in other habitats. Other dead leaf probers such as Ancistrops
strigilatus and Automolus infuscatus were only captured once, and were very rare in our
samples. In chamizal, fewer species were captured in all three of these foraging
substrate categories. Sally-glean foraging species that should prefer more open habitats
did not show any differences in capture rates among the habitats, and the number of
species did not vary either, which suggests that both forest types offer rich and varied
opportunities for species that search for insects far from their perches.
While capture rates did not differ for nectarivores and frugivores, both of these
foraging guild showed higher observed species richness in dry and humid varillal forests
(Fig. 3-9A & Fig. 3-9B). Nectarivore capture rates also showed a marginal trend towards
higher capture rates in terra firme clay forests. However, these idiosyncratic patterns
can be explained by the frequent movement of these birds through the forest, because
most species were traplining hermit species (Stiles 1975). Contrary to our predictions,
primarily frugivorous species were not captured more often in either habitat, but this
guild was more speciose in white sands. We predicted higher abundance of frugivores
in WSF because the understory vegetation is known to be richer in melastomes in WSF
than in clay soil forests (Ruokolainen et al. 1997, Thessler et al. 2005, Ruokolainen et
al. 2007, Pomara et al. 2012). Nevertheless, the diversity of frugivores was high in WSF
and one migratory frugivore that had a previously unknown winter distribution and
habitat preference, was almost entirely restricted to WSF (Ungvari-Martin et al. 2016).
Many specialist plants in white sand forests exhibited mast fruiting (Janzen 1974,
Macedo and Prance 1978, Arbelaez and Parrado-Rosselli 2005), producing cyclical
68
resources and periods of fruit scarcity. The commonest species detected by Alonso et
al, Claravis pretiosa, which was essentially absent during our study years, belongs to a
genus that exploits masting species such as bamboo. A canopy frugivorous cotinga
(Xipholena purpurata) that was common in 2006 (Robinson, pers.obs) was only rarely
observed.
Some of the biases associated with mistnet samples were not as strong as we
had predicted. There was a slight increase in capture rates of canopy birds in the
shortest WSF, but this pattern was not statistically different. We acknowledge that mist-
netting provides a biased sample of the bird community (Remsen Jr and Good 1996).
For these reasons, we excluded canopy birds from the comparisons and rarefaction
analyses (Fig. 3-11) for non-canopy species only show similar asymptotic species
accumulation curves and similar differences between clay soil and the WSF habitats.
Although our results from mistnet captures were generally in accord with the
results from song censuses from this particular Peruvian white sand forest site (Alonso
and Whitney 2003, Alvarez Alonso et al. 2012), there were some notable differences.
The results described above are not completely in concordance with the initial published
findings. We confirmed that Electron platyrhynchus, Bucco tamatia, Xiphorhynchus
guttatus, Pigyptila stellaris, Myiobius barbatus, and Saltator grossus were species rare
or absent from white sand forest patches, but present in the AMNR. However, in WSF
we did consistently capture Thamnomaes ardesiacus and T. caesius, Myrmeciza fortis,
Machaeropterus regulus, Manacus manacus, Lepidothrix coronata and one individual of
Turdus lawrencii. We also recorded recaptures for these species, suggesting that WSFs
are not simply serving as holding areas for young, non-breeding floaters that are just
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moving through this habitat or residing in it for short periods while waiting for territories
in clay forest to open up. Thus, the WSFs were not as species-poor as originally
thought, at least in the taller humid WSF. More recent publications from this area also
discuss a growing list of apparent habitat specialists from the site (Álvarez Alonso et al.
2013).
Floristic composition, forest structure and arthropod assemblages vary across
different Amazonian forest types (Vormisto et al. 2000, Saaksjarvi et al. 2006, Fine
2010, Lamarre et al. 2012, Lamarre et al. 2016b). Most arthropod functional groups
showed higher relative abundances in terra firme clay forests than in WSF on a large
regional scale (Lamarre et al. 2016b), and the same pattern holds for Lepidoptera
assessed on a local scale as well (Lamarre et al. 2016a). However, the abundance and
species richness of Hymenoptera differ from the above pattern, and the species
composition of this group is highly heterogeneous and does not correlate to floristic
composition or productivity patterns (Saaksjarvi et al. 2004, Fine et al. 2006, Saaksjarvi
et al. 2006, Gomez et al. 2015). These differences in forest structure, composition, and
arthropod community composition can scale up to explain the slight differences we
found in the bird communities inhabiting WSF habitat and terra firme clay forests.
Further analyses in fine scale community patters, and more chamizal sampling could
elucidate the differences and the mechanisms behind them.
The most important finding from our dataset is how similar WSF is to the
surrounding terra firme matrix. Despite the differences in vegetation, the low productivity
forest growing on sandy soil is able to maintain a diverse community of birds.
Understanding the factors that regulate patterns of species diversity, turnover and
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richness of communities is a fundamental ecological problem, and productivity has been
a focus of many studies (Mittelbach et al. 2001). Without direct measure of productivity,
vegetation structure can serve as a proxy for comparing forested habitats, but the
differences in productivity change so many different aspects of community structure that
it is not surprising that we found a variety of guild-specific responses to plant
communities growing on different soil types. Apart from floristic differences, it may be
structural complexity alone driving bird communities and bird species diversity, just as
described by MacArthur’s classic and highly cited hypothesis that the vertical profile of
foliage drives bird species diversity (MacArthur 1961, Macarthur 1964, Karr and Roth
1971, Pearman 2002).
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Table 3-1. Vegetation structure environmental variables used for comparisons of the four habitat types. All values represent mean values for habitat types ± standard deviations, and these variables had significant differences among habitat types.
HABITAT Canopy Height (m)
Leaf litter Depth (cm)
DBH trees (cm)
Number of Large Stems
Number of Small Stems
Clay 17.38 ± 4.6a 36.12 ± 20.1a 21.06 ± 5.3a 35.25 ± 9.8a 45.21±31.5a
Humid 15.71 ± 3.9b 46.8 ± 24.1bc 19.43 ± 4.9a 14.71 ± 10.1a 46.48±25.7a
Dry 14.34 ± 3.5c 41.72 ± 20.5bc 18.87 ± 4.5b 13.66 ± 7.4a 59.58±37.4b
Chamizal 7.5 ± 1.15d 50.5 ± 12.7ab 16.48 ± 4.8b 12.19 ± 7.9b 69.56±18.3b
Different superscripts abcd denote statistically different values.
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Table 3-2. Dietary guild comparisons of the four habitat types. We assessed community composition using broad dietary categories and no differences were found in the trophic organization of the understory bird community.
Dietary Guild clay humid dry chamizal
Insectivores 0.613208 0.59596 0.622642 0.54717
Omnivores 0.188679 0.181818 0.141509 0.169811
Frugivores 0.084906 0.10101 0.103774 0.169811
Insectivores-Nectarivores 0.066038 0.090909 0.103774 0.09434
Carnivores and piscivores 0.04717 0.030303 0.028302 0.018868
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Table 3-3. Capture rate comparisons for all birds and for select guilds or functional groups in the four habitat types using individuals per 500 net hour units. All values represent mean number of individual birds for the guild in the habitat types ± standard deviations.
Guilds Clay Humid Dry Chamizal MANOVA
Total 100.37±31.61 88.89±23.32 88.32±31.74 66.67±9
Mixed Species Flocks 10.22±6.15 6.52±3.65 6.35±3.57 5±2.28 **
Shrub Gleaners 42.68±13.86 29.3±11.12 28.9±10.09 20.33±4.5 ***
Ant Followers 12.26±5.67 11.81±5.31 14.98±7.13 9.67±5.35
Terrestrial Insectivores 2.33±1.78 2±1.23 1.76±0.97 1.5±0.71 ***
Dead Leaf Probers 2.94±1.55 3.13±1.46 2.11±1.1 2.2±1.3 *
Sally-Gleaners 6.23±3.13 5.54±2.59 6.4±3.22 5.5±1.38
Nectarivores 6.21±2.59 3.89±2.5 5.32±3.42 2.6±1.52 *
Non-canopy Frugivores 7±3.53 6.27±2.65 5.46±2.69 5±2.53
MANOVA Significance codes: ‘***’p< 0.001, ‘**’ p<0.01, ‘*’ p<0.05
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Table 3-4. Diversity indices comparisons of bird communities of the four habitat types for all species and for white sand forest specialists alone from the AMNR capture data. The last column indicates significance levels of ANOVA statistical test, numbers represent mean values with standard deviations.
Comparison Subset Clay (n=19)
Humid (n=27)
Dry (n=41)
Chamizal (n=6) P
Mean Capture all species 100.37±31.61 88.89±23.32 88.32±31.74 66.67±9
Recaptures all species 0.18±0.16 0.22±0.22 0.2±0.29 0.17±0.33
No. Species all species 39.55±15.44a 37.36±9.91a 36.65±10.1a 30.33±5.51b *
Species Recaptured all species 0.21±0.47 0.32±0.4 0.31±0.43 0.27±0.61
Shannon Index all species 3.11±0.3a 2.7±0.3b 2.8±0.21b 2.7±0.21b ***
Simpson Index all species 0.94±0.03a 0.89±0.05b 0.91±0.02b 0.90±0.03ab *
Fisher’s Alpha all species 19.69±6.1a 12.39±3.7b 12.7±3.1b 13.1±5.1b ***
Mean Capture specialists 4±2.67 6.16±4.13 6.88±3.51 7.17±3.19
Mean No. Species specialists 1.5±0.85 2.16±1.18 2.76±1.24 4.17±0.41
Recaptures specialists 0.18 ± 0.33 0.09 ± 0.15 0.14 ± 0.26 0.2 ± 0.18
Shannon Index specialists 0.29 ± 0.48a 0.63 ± 0.55b 0.91 ± 0.49b 1.42 ± 0.09c ***
Simpson Index specialists 0.18 ± 0.3a 0.39 ± 0.31b 0.54 ± 0.25b 0.76 ± 0.02c ***
Fisher’s Alpha specialists 21.04±061a 21.4±0.85a 21.15±0.99a 22.33±0.73b ***
Significance codes: ‘***’p< 0.001, ‘**’ p<0.01, ‘*’ p<0.05 Different superscripts abc denote statistically different values.
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Figure 3-1. Comparison of number of species between two general habitat types.
Slightly more species were captured in clay soil forests than in white sands forests per sampling event (t-test, p= 0.01043), raw data are represented in the figures, log transformed data were used for statistical test.
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Figure 3-2. Rank abundance curves for white sand forests and clay forest captures.
Species are arranged according to their rank order abundance in clay soil forest, since terra firme is the dominant forest type. WSF ranks show differences in a few species.
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Figure 3-3. Forest canopy height in the four different habitat types, letter codes refer to
statistical differences (ANOVA, p<0.05), red points indicate outlier values, letter codes a-b-c-d indicate statistically different values.
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Figure 3-4. Cumulative distribution of species occurrences over all netlines (n=93
sampling events). Each point represent a species, and the number of sampling events at which that species was recorded. The number of species with very few occurrences is high, and the distribution is skewed, where most species were only recorded at a few sampling events.
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Figure 3-5. Species accumulation curves for four different habitat types. Rarefaction
analysis was performed to standardize comparisons with effort.
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Figure 3-6. Turnover, dissimilarity and nestedness between white sand and clay soil
forest bird communities.
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Figure 3-7. Ordination diagram based on Bray-Curtis Dissimilarity using netlines as
sites for all WSF and clay habitats. The grey lines connecting the points result from the hierarchical clustering analysis using the average linkage method.
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A)
B)
Figure 3-8. Comparison of capture rates and number of observed species of flocking or
non-flocking shrub gleaner insectivores. A) Species that are members of mixes-species foraging flocks, and non-flocking shrub gleaners were captured more in terra firme clay than in the WSF habitats (p= 0.0057 **, and p= 0.000014 ***,mean captures per sampling event with standard deviations). B) The observed number of species per guild in each habitat type varied little except for the notable loss of species in chamizal habitat.
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A)
B)
Figure 3-9. Comparisons of captures and species according to foraging methods. A)
Capture rates of dead-leaf probing species and terrestrial insectivores varied somewhat between terra firme clay and in the WSF habitats, army ant follower and sally-gleaners were captured at similar rates. B) The observed number of species per guild is each group dropped considerably in the chamizal habitat. The highest number of sally gleaner species was captures in dry varillal.
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A)
B)
Figure 3-10. Comparisons of captures and species according to diet A) Both
nectarivore species and frugivores were captured at approximately equal rates on average in terra firme clay and in the WSF habitats, though somewhat fewer captures in humid varillal B) The observed number of species per guild increased between clay, humid and dry varillales, and dropped considerably in the chamizal habitat.
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Figure 3-11. Rarefaction based species accumulation curves show the differences for
white sand forest habitats and clay habitat. Overlapping 95% confidence intervals are interpreted as no difference.
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CHAPTER 4 MOVEMENT, ISOLATION AND GENE FLOW AMONG PATCHES OF WHITE SAND
FORESTS
Introduction
One of the most critical questions in the origin and maintenance of species
diversity is the extent to which isolation of habitat patches affects species composition,
dispersal, gene flow and, thereby, incipient speciation, local evolution and
differentiation. In the Amazon, some habitat patches such as riverine habitats (igapo,
varzea, Mauritia palm swamps) are intrinsically connected as they form along linear
habitat features such as rivers, but other habitats such as white sands forests (see
Chapter 2) occur in isolated patches embedded in a matrix of terra firme clay soils
(Pitman et al. 2008, Stropp et al. 2011). Bamboo and river islands are ephemeral, can
vary greatly in size, and require life-history adaptations that maximize dispersal ability to
find patches (e. g., when the bamboo dies) (Kratter 1997, Santana and dos Anjos
2010).
White sands forests form on essentially permanent soil formations and often
consist of small, isolated patches or archipelagos (Anderson 1981, Brown and Prance
1987, Prance 1996, Borges et al. 2016). While the white sand ecosystem is widely
distributed throughout Northern Amazonia, the area of patches ranges from as small as
less than one square kilometer to thousands of square kilometers (Adeney et al. 2016).
Terra firme clay soils surround these patches (Anderson 1981, Stropp et al. 2011),
potentially creating barriers to gene flow among white sand patches (Capurucho et al.
2013). Such habitat patches therefore have the potential to be crucibles of evolution in
which local differentiation can take place if gene flow is indeed limited (Frasier et al.
2008). The small size and isolation of many WSF patches probably contributes to the
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large number of endemic species and their low species richness in general (Steege et
al. 2000, Adeney et al. 2016). Yet, the community analyses in Chapter 2 and Chapter 3
suggested that WSF patches have very similar community composition to other forest
types, which suggests that there may be fewer barriers to gene flow. In this chapter, we
take this analysis one step further by examining the extent to which patches of one
habitat type—white sands forests—are interconnected within a region.
AMNR forms the largest archipelago of white sand forest islands in Western
Amazonia, and this area is somewhat isolated from the larger expanses of sandy
Amazonian habitats within the same biogeographical region (Fine et al. 2010, Álvarez
Alonso et al. 2013, Adeney et al. 2016). The reserve is located west of the Napo River,
and north of the Amazon River, within the Napo ecoregion. A subset of white sand
forest specialist bird species inhabit the reserve; not all species can be found here that
are considered WSF specialists (Alvarez Alonso 2002, Alonso et al. 2013). Comparative
studies have been carried out in the past decade (Pomara et al. 2012, Álvarez Alonso et
al. 2013) evaluating bird species composition in the region We used detailed maps of
the white sand islands to evaluate the species-area relationship in the white sand
habitats and the effects of the degree of isolation on the patches.
The questions we were interested in were the following: First, does patch size
and distance between patches affect species richness in white sand forests? Second,
do white sand forest specialists ever occur in the matrix outside of WSF patches? And
third, can we find evidence of dispersal or gene flow among these patches? We
hypothesized that species richness of habitat specialists in a habitat patch could be
predicted by its area, and therefore we predicted that larger white sand forest patches
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would harbor more species of white sand specialists (MacArthur and Wilson 1967,
Connor 1979, Lomolino 2000, Lomolino 2001). If species were randomly distributed
among patches, the resulting curve of species composition and patch area would rise
gradually. If only small subsets of species survive on small patches, then we would
expect an S shaped curve, indicating a threshold patch size where species richness
would level out.
Patch isolation should also affect community composition if WSF specialists are
dispersal-limited. If specialists are less likely to move longer distances across the matrix
habitats, we predicted that isolated patches would have fewer WSF specialists.
Alternatively, there may be no difference in community composition, which would
suggest that the clay soil matrix does not act as a significant barrier to dispersal. In this
case, the patchy nature of their habitat may select for a bimodal distribution of dispersal
distances in WSF specialists with a high incidence of very short movements that keep
the birds within their patches, and a smaller number of very long-distance movements
between different patches compared with habitat generalists, which should have more
intermediate length movements.
In addition to field observations of potential movement, we inferred dispersal
among WSF patches by estimating gene flow among the populations. The genome
provides a record of the demographic processes acting on a given lineage. The diversity
in a population and the divergence between individuals allows us to elucidate the
processes that shape the diversity. Isolation-by-distance describes the tendency of
individuals to find mates from nearby populations rather than distant populations (Wright
1943). As a result of this tendency, populations that live near each other are genetically
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more similar than populations that live further apart. Alternatively, patchy habitats can
produce signatures of small island population models, which predict abrupt changes
among the populations on different islands (Wright 1943, Slatkin 1993). Populations of
obligate white-sand specialists living on different white sand forest patches are
connected by dispersal on a local scale but may become increasingly isolated on the
regional scale.
We examined genetic variation and migration rates within and among
populations and we examined these variables for a white-sand specialist focal species
(Neopelma chrysocephalum) living in a landscape in which patches of white sands
forests are embedded in a matrix of clay sand forest. The genus Neopelma exhibits
largely allopatric, almost circum-Amazonian distribution with five recognized species;
together with the genus Tyranneutes, which is sister to all other taxa of manakins, they
form one of the basal lineages of the family Pipridae (Tello et al. 2009, McKay et al.
2010). N. chrysocephalum is widely distributed across the Guiana Shield, but it was only
recently documented in Northeastern Peru in white sand forests, where it is fairly
common (Alonso and Whitney 2003), its behavior and distribution throughout the range
remain poorly known (Kirwan and Green 2011). If the clay forest matrix in which these
patches are embedded act as dispersal barriers, we predict that genetic differentiation
among populations would increase with distance. Alternatively, if the matrix is not an
effective barrier, then we predicted that there would be little difference in the genetic
structure of populations among patches.
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Methods
Study Landscape
We used the AMNR study areas, regions and netlines represented on the map
(Fig. 4-1). We evaluated species composition as a function of patch size and insularity
or isolation from other patches of similar habitat. We plotted each sampling event for
each area cumulatively. We plotted all species as well just the white sands habitat
specialists as defined by Alvarez et al. (2002). The cumulative number of species using
the Chao estimate of richness (Gotelli and Chao 2013) was plotted against area of each
patch and region. Area calculations were carried out with the raster calculator in
ArcMAP version 10.0 (ESRI 2009). We then used several approaches based on species
richness estimations to examine variation in bird species composition and to evaluate
correspondence of bird communities with patch size.
Species-area Relationships
We fitted different models for species richness and patch area, patch isolation,
and the area of the nearest neighboring patch, and applied AIC model selection to test
for area effects within white sand habitats. A model selection approach based on AIC
(Burnham & Anderson 2002) was applied to select among possible hypotheses
regarding the source of variation in diversity or composition indices (i.e., effects of area,
distance, or both). Species totals for each “island” were averaged and linear models of
species richness as a function area were constructed with log-transformed variables,
models were compared using AIC.
Molecular Analysis
Blood samples taken from 141 individuals during mist-netting sessions were
used to quantify between-patch gene flow on different spatial scales in the habitat
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specialist species Neopelma chrysocephalum. Genomic DNA was extracted from blood
samples using Qiagen DNeasy kits (Quiagen, Inc., Valencia, California, USA) following
the manufacturers protocol. All samples’ DNA concentrations were quantified using
nanodropping, and the mean fragment size of each sample was further assessed using
gel electrophoresis.
We used a double digest RAD sequencing (ddRADseq) approach to obtain
variable genetic markers for the population level sampling (Barber et al. 2004). The
ddRADseq libraries for each individual were prepared using a slightly modified version
of the protocol from Peterson et al (2012). Each sample was assigned a unique 8-9-10-
or 14 basepair (bp) long inline barcode, equimolar concentration of uniquely barcoded
individuals were pooled and double indexed by 11 cycles of high fidelity PCR. The PCR
products were pooled and sequences in one lane of a high throughput run of on a
NextSeq 500 sequencer (Illumina, San Diego, CA, USA) at the ICBR of University of
Florida, producing 150-bp single-end reads. A description of detailed methods on
sample preparation is available in Appendix C.
All samples were sequenced at the University of Florida Cancer and Genetics
Research Center in a single lane of the NextSeq 500 following a sequencing protocol
for 150bp single-end reads. We prepared the raw Illumina FASTQ files for analysis by
processing the sequence data using the program process_radtags in STACKS version
1.35 (Catchen et al. 2013). First, we sorted the read pairs by barcode, and removed any
reads that did not contain both a correct barcode and the remaining bases of the
restriction site sequence. When demultiplexing the samples, we did not allow for
sequencing error in the barcodes and discarded reads that included low quality sites
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using the Phred quality scoring system, only keeping reads with greater than 33 quality
score reads. We retained 83.52% of the raw reads, and 91.2% of the reads had higher
than 30 quality scores (Appendix D, Table D-1). We retained 316,455,808 reads 130 bp
or greater in length, and trimmed all reads to 130 bp for the STACKS pipeline that
requires uniform read length. The reads were merged into a single file per individuals,
followed by catalog building using denovo_map.pl Perl script. We used a minimum
depth of coverage (-m) of 2 to create stacks, a maximum nucleotide distance between
stacks (-M) of 3 when identifying the loci, and a maximum number of mismatches
between sample tags (-n) of 2 when generating the catalog (Catchen et al. 2013).
These parameters were chosen following Catchen et al 2013. We enabled the
Deleveraging and Removal algorithms to filter out highly repetitive likely paralogous loci.
These analyses were carried out on the University of Florida HPC server (HyperGator).
Population structure analysis
We eliminated some individuals due to low sequence coverage, then clustered
the reads into RAD loci, identified the RAD loci containing SNPs and genotyped all
samples by calling SNPs in each individual (Appendix D, Table D-2) using a maximum
likelihood statistical model implemented in STACKS (Davey et al. 2011, Hohenlohe et
al. 2011, Hohenlohe et al. 2012). Then, we used load_radtags.pl and index_radtags.pl
to locally populate and index a MYSQL database of loci. Using the scripts export_sql.pl
and populations.pl, we selected the polymorphic loci that were present in 90% of the
individuals. We only selected the first biallelic or triallelic SNP at loci that contained
multiple SNPs to reduce the possible effects of linkage. In addition, using the script
populations.pl we calculated several genetic diversity parameters (expected and
observed heterozygosity, nucleotide diversity and inbreeding coefficient) based on one
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SNP for each RAD locus for each of the putative geographic populations and the whole
landscape level metapopulation.
Information on the RAD loci identified will be submitted to Dryad, including
information on each locus present in at least 120 individuals and consensus genotypes
for each individual. Appendix D Table D-1 shows the number of reads obtained in the
run and after the quality filtering steps. We only used loci present in all putative
populations, and in at least 75% of the samples. We also calculated several genetic
diversity parameters for separate groups and overall, basic indices of genetic diversity
within populations, such as degree of heterozygosity, number of alleles or number of
polymorphic loci (Appendix D Table D-3). STACKS assembled a catalog containing a
raw total of 349,166 loci. 144,003 of the total were polymorphic loci, an after filtering
procedure in populations with the specific resulted in levels of completeness ranging
from 80-100%. After filtering for a minimum stack depth of 5 and presence in at least
75% of the individuals of all populations 87125 loci were retained. From this set of loci,
at least one SNP variable sites were found among 49155 of the loci, resulting in a data
matrix was 75 % complete across 132 individuals in the 8 putative populations defined
by WSF patches. We further filtered loci for low minor allele frequency across the
dataset (>10%) to avoid not being able to tell the difference between rare alleles and
sequencing error.
We conducted three different tests to identify population structure. First we
calculated population differentiation FST statistics with STACKS using the population
program. Then we used the program STRUCTURE, version 2.3.4 (Pritchard et al. 2000)
to establish the number of distinct populations through cluster analyses based on the
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genotype data alone, as well as GENODIVE (Meirmans and Van Tienderen 2004),
which has unique functions that are not implemented in other programs including
Analysis of Molecular Variance. We evaluated genetic diversity and allelic richness in
populations that occur in different patches. In STRUCTURE we used possible K values
(number of populations) ranging from 1-8, one population per sampling region. We used
20 runs for each K value. The Markov chain was run for 5.0 × 105 steps per analysis,
with a burn-in of 1.0 × 105. Log likelihood values were averaged across runs for each K
value, and these values were evaluated along with patterns of individual assignment to
clusters to draw inference regarding the optimal K value for each dataset. Results were
summarized with STRUCTURE HARVESTER (Earl and vonHoldt 2012), figures were
prepared using the R package adegenet (Jombart 2008, Jombart and Ahmed 2011).
Results
Movements Detected by Recaptures
Most species in the sample were recaptured on the same netline and within the
same patch across years. We intentionally scheduled sampling dates to correspond
with the previous year’s sampling dates as close as possible. Only Dendrocincla
merula, a large ant-follower woodcreeper, was consistently recaptured on different
netlines; over 25% of recaptures of this species between years occurred on a different
netline (Table 4-1). Across all species, most movements were relatively short distance,
between 300-700m, but some individuals were captured as far as 6km from the initial
capture site. The second widest-ranging species, Pithys albifrons, was also an obligate
ant-follower species that was not restricted to white sands forests (Table 4-1). Of the
WSF specialists, only Neopelma chrysocephalum was recaptured on a different netline
(see below).
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Species – Area Relationships
We estimated species richness for all captured species as well as just the habitat
specialists on each WSF habitat patch. Habitat patch sizes varied from as small as 6
hectares to over 800 hectares (Table 4-2). White sand forest habitat patches were
separated from each other by distances ranging from 435m to 3.3km, and each patch in
our analysis had another WSF patch at least 5 hectares in size within those distances
(all localities and spatial data are summarized in Table 4-2). According to the AIC model
testing, the best model to explain the patterns of species richness for both sets was the
full model that included all three factors (patch area, the area of the nearest patch, and
the distance to the nearest patch) with a Poisson distribution (Table 4-3). The results do
not show strong support for patch dynamics in this system, and none of the spatial
variables explained the variation in species richness. White sand forest specialist
species can be found in all of the patch sizes, and estimated species richness in this
subset of species does not increase with patch area, as we predicted (Fig. 4-2).
Genetic Diversity and Population Assignment Tests
We obtained estimates of genetic diversity of Neopelma chrysocephalum in each
population, examining polymorphic loci only, for the most complete dataset of 2240
biallelic loci and 129 individuals (Table 4-4). Pairwise FST values (Table 4-5) calculated
for the data set were consistent across all methods, and showed little to no genetic
differentiation among the populations. We found evidence for panmixia, and identified 9
individuals that were migrants in the population using GenoDive, as well as using
STRUCTURE’s admixture analysis. Genetic distances between inferred populations
and the observed populations were consistent between STRUCTURE and GenoDive.
We tested population differentiation, and consistently, across all datasets including all
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genotyped individuals and various subsets of markers, the best clustering was a single
cluster of individuals k=1 (Fig. 4-3) according to Bayesian Information Criterion (BIC)
using among clusters sums of squares from an AMOVA, and also using Euclidean
distances calculated from within-individuals allele frequencies implemented in GenoDive
(Meirmans and Van Tienderen 2004). Taken together the analyses all suggest high
levels of gene flow throughout the populations.
Discussion
In this study, we found evidence that habitat specialization does not necessarily
prevent birds from moving across the matrix of a different habitat. One individual of the
obligate habitat specialist Neopelma chrysocephalum exhibited an inter-patch
movement (~6.5 km) that can be considered a dispersal event. In addition, white sand
specialist species were found in all patch sizes, and the distance between patches also
did not explain their presence and the estimated species richness of specialists,
indicating that the birds can move across other forested habitats as well. In combination
with the strong similarity in communities of forested habitats documented in Chapter 2,
we can conclude that dispersal does not strongly constrain bird communities of WSF.
Two species of ant-following birds exhibited the largest proportion of birds
recaptured at distant sites: Dendrocincla merula and Pithys albifrons. These data imply
either very large home ranges or a large area over which young birds disperse. Large
home ranges are especially likely because these species are known to move through
the forest understory searching for ant swarms (Willis and Oniki 1992, Schulenberg et
al. 2007). We observed only a single Neopelma chrysocephalum moving across
patches: this bird moved 6.5 km between 2010 and 2011, and then another 670 m
between 2011 and 2012. Based on the brown blotches in the eyes, we identified this
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bird as a juvenile in 2010. In the subsequent years, the bird’s iris color changed to
creamy yellow. The same bird was captured twice in 2012 on the same netline. Other
individuals of Neopelma that moved only traveled between 350m-450m distances from
initial to subsequent capture sites (n=3).
The circum-Amazon distribution of the genus Neopelma implies that this species
either disperses readily now or did so in the past. There are currently no studies of the
phylogenetics or the population genetics of any members of this genus. Recently, a
study examined the population genetics of another white sand habitat specialist
manakin species, Xenopipo atronitens (Capurucho et al. 2013) on a larger geographic
scale. Consistent with our findings: they found no strong isolation-by-distance among
sites within the landscape, and geographic distance explained very little of the variation
in genetic distance.
Interestingly, landscape genetic analyses indicate that terra firme forests are less
permeable to dispersal, acting as a barrier to gene flow for X. atronitens. According to
our results of mark-recapture as well as genetics, Neopelma chrysocephalum can move
through the terra firme forest, at least on the spatial scale of white sand archipelagos.
Inventories in campina habitats reveal a widespread bird community with little local
endemism, meanwhile the Western Amazonian white sands harbor seemingly disjunct
populations of some species. Therefore, further genetic studies of white sand specialist
birds can complement the current knowledge about Amazonian landscape history,
revealing patterns that have not been detected so far.
Studies of migration rate often rely on two approaches: a coalescent approach,
which uses the genealogical information contained in DNA sequences, and a multilocus
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genotype approach. Generally, the coalescent approaches (e.g., those implemented in
the program MIGRATE; Beerli and Felsenstein 2001) estimate long term evolutionary
parameters in populations geographically isolated with little migration over longer time-
scales, whereas the genotype approach estimates short-term parameters in populations
in the same geographical area with potential migrants every generation. We can
implement our data in the future using coalescent approaches to estimate effective
population size and assess the long term stability of the population.
The persistence of a population depends on dispersal across fragmented
habitats (Hanski 1994, Moilanen 1998, Thomas and Kunin 1999, Van Houtan et al.
2007). Biogeography and ecology in Amazonia reflect consequences of regionally
varying environmental factors, both past and present. Better understanding of these
factors is critical to reconstructing the evolution of Amazonian biota, and to be able to
plan and manage Amazonian ecosystems in a sustainable manner. WSF bird
communities appear to be well adapted to persist as long as some dispersal corridors
remain to connect the patches, and even small patches can harbor the habitat specialist
species.
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Table 4-1. The twenty most captured bird species, and the recaptures and movement proportions of those species. Movements are defined as recaptures on a different netline.
Species Number of Banded birds
Recaptures between years (%)
Movements (%)
Year-to-year movements (%)
Dendrocincla merula 138 52.90 44.20 25.36 Pithys albifrons 230 29.13 20.43 8.26 Dendrocincla fuliginosa 78 15.38 10.26 7.69 Willisornis poecilonotus 270 27.04 5.56 4.44 Xenops minutus 46 26.09 8.70 4.35 Xiphorhynchus elegans 138 21.01 5.80 4.35 Myrmoborus myotherinus 98 12.24 4.08 4.08 Dixiphia pipra 469 16.20 5.54 4.05 Schiffornis turdina 58 20.69 6.90 3.45 Mionectes oleagineus 332 8.73 3.31 3.31 Glyphorynchus spirurus 875 26.51 5.14 3.09 Gymnopithys leucaspis 209 21.05 9.57 2.87 Neopelma chrysocephalum 141 25.53 3.55 2.84 Lepidothrix coronata 145 8.28 2.76 2.76 Myrmotherula axillaris 159 9.43 3.14 1.89 Myrmotherula hauxwelli 127 10.24 2.36 1.57 Megastictus margaritatus 135 11.85 5.19 1.48 Automolus ochrolaemus 74 18.92 2.70 1.35 Corythopis torquatus 45 24.44 2.22 0.00 Percnostola arenarum 34 20.59 2.94 0.00
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Table 4-2. Localities, area of WSF patches, distances to nearest patch, and area of nearest patch to the sampled patch. Lat/lon coordinates are in UTM geographic coordinate system, UTM 18S.
Lon Lat Area (ha)
Distance to near patch (m)
Area of near patch(ha) REGION Habitat
674665.6 9560322 6.77 1643.98 15.19 km 28 Dry
672846.1 9558681 51.75 981.49 68.86 km 31 Dry
660513.5 9566505 87.16 909.15 51.91 Porvenir Humid
667166.6 9568501 102.72 954.15 7.63 Mishana Dry
659549.4 9566840 116.18 1020.57 87.16 Porvenir Humid
673338 9564324 124.30 1623.89 14.00 km 28 Chamizal
673243.4 9570139 148.43 1206.41 37.69 San Martin Humid
675195.2 9570028 186.09 781.53 19.70 San Martin Humid
662287.1 9567414 208.84 435.16 208.84 Yuto Dry
674455.4 9568191 219.07 1378.30 15.29 Nueva Esperanza
Humid
676660.3 9563815 255.21 3361.10 124.30 km 25 Humid
670579.1 9568112 847.83 2091.88 37.69 Mishana Dry
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Table 4-3. Model selection results of the explanatory values of area of WSF patches, distances to nearest patch, and area of nearest patch to the sampled patch.
Models Distribution Intercept Area Near Area Near Dist AIC
Area Poisson 3.716 0.0004534 NA NA 49.5279
Area Negative Binomial
3.722 0.0004314 NA NA 50.1910
Near Area Poisson 3.960 NA -0.0013457 NA 51.7558
Near Area Negative Binomial
3.958 NA -0.0013171 NA 51.0828
Near Dist Poisson 4.008 NA NA -0.0001009 51.4814
Near Dist Negative Binomial
4.022 NA NA -0.0001101 50.8433
Full Poisson 4.079 0.0006489 -0.0012541 -0.0002117 45.1498
Full Negative Binomial
4.079 0.0006489 -0.0012541 -0.0002117 47.1499
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Table 4-4. Summary of sampling locality, samples in the locality, and summary of raw sequence information for those samples.
Region Samples Mean raw reads
Mean unique stacks
Mean polymorphic loci
km 25 9 1.77x105 4.97x104 2.19x104
km 28 14 2.19x105 6.81x104 2.50x104
km 31 6 1.13x105 9.76x104 1.29x104
Mishana 34 1.66x105 5.13x104 1.95x104
Nueva_Esperanza 31 1.48x105 6.12x104 1.69x104
Porvenir 27 1.62x105 4.92x104 1.79x104
San_Martin 20 1.68x105 5.35x104 1.94x104
Yuto 37 1.79x105 4.84x104 2.40x104
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Table 4-5. Summary of population differentiation results based on FST calculations.
KM_28 San_Martin Mishana Yuto Nueva_ Esperanza km31 Porvenir km_25
KM_28 0 0.0082 0.0067 0.007 0.0061 0.0133 0.0067 0.0147
San_Martin 0 0.0043 0.0049 0.0046 0.0113 0.0046 0.0103
Mishana 0 0.0041 0.004 0.009 0.0045 0.0085
Yuto 0 0.0038 0.0093 0.0046 0.0073
Nueva_Esperanza 0 0.0095 0.0043 0.0103
km31 0 0.0097 0.0204
Porvenir 0 0.0102
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Figure 4-1. Map of white sand forest patches including the 150 m buffer from the edge
for each forest island. The names of the putatively isolated regions are included on the map.
105
Figure 4-2. The relationship between species richness and the area of the sampled
white sand patch, the area of the nearest WSF patch and the distance to the nearest WSF patch. Blue points represent WSF specialist species.
106
Figure 4-3. Population genetics results based on k-means clustering as well as
principal component analysis based on allele frequencies indicate little differentiation between regional populations, and a single genetic cluster.
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CHAPTER 5 INTER-ANNUAL SITE FIDELITY AND BREEDING ORIGINS OF GRAY-CHEEKED
THRUSHES IN WHITE SAND FORESTS OF THE PERUVIAN AMAZON 1
Introduction
The annual migration of Gray-cheeked Thrushes (Catharus minimus) from boreal
forests in northern North America and eastern Siberia to Amazonian rainforest is among
the longest by a Nearctic-Neotropical migratory songbird with some individuals making
a 20,000 km round trip. Most of the annual cycle of Gray-cheeked Thrushes is spent in
South America yet we are lacking knowledge about their non-breeding ecology in the
tropics, including movements, ecological roles, and seasonal habitat use. Recent
advances in tracking technology have revealed that Catharus thrushes may undertake
significant intratropical movement than can span much of their non-breeding season
(Heckscher et al. 2011, Cormier et al. 2013, Heckscher et al. 2015, Hobson et al.
2015). Although we lack details regarding movements of individual Gray-cheeked
Thrushes in South America, the possibility of seasonal movements hinders our ability to
draw inference regarding the habitat associations of sedentary birds because banding,
specimen, or sight records may represent individuals at stopover or temporary sites
rather than settled birds. This gap in our knowledge limits our ability to delimit accurate
winter ranges, assess threats, and convey conservation needs. The breeding origin of
Nearctic-Neotropical migratory birds occupying specific regions in South America and
1 Reprinted with permission from Ungvari-Martin, J., C. M. Heckscher, and K. A. Hobson. 2016. Inter-
annual site fidelity and breeding origins of Gray-cheeked Thrushes in white sand forests of the Peruvian Amazon. Journal of Field Ornithology 87:55-64.
108
the connectivity between breeding regions and non-breeding sites is of interest to
conservationists (Hobson et al. 2014).
Only recently have advances in tracking technology and stable isotope analyses
begun to reveal that connectivity (Fraser et al. 2012, Cormier et al. 2013, Gallo et al.
2013, Hobson et al. 2014). Although the understanding of connectivity between
wintering and breeding regions is widely considered of paramount importance (Faaborg
et al. 2010)(Webster and Marra 2005, Faaborg et al. 2010), from a conservation
perspective, understanding habitat associations and the movement and settlement
patterns of Nearctic-Neotropical migrants in tropical regions is equally important
especially if populations depend on rare or uncommon tropical ecosystems.
White sand terra firme forests are scattered throughout lowlands of the western
Amazon basin often amid weathered clay terra firme forests. White sand systems differ
from clay forests in their unusual floristic composition, sandy substrate, scleromorphic
physiognomy, and low nutrient content (Anderson 1981). These forests cover just 3% of
the Amazon basin (Pennington 2009) and effectively function as ecological islands in
the vast forests of Amazonia (Prance 1996). They maintain distinct floral assemblages,
including many endemic plant species, and have been examined for their importance in
maintaining obligate and unusual assemblages of birds (Alvarez Alonso and Whitney
2003). Importantly, the use of white sand forests in western Amazonia by Nearctic-
Neotropical migratory songbirds has not been evaluated.
We report the capture of Gray-cheeked Thrushes over three field seasons in
white sand forests in the Amazon basin near Iquitos, Peru. The thrushes were captured
during a broader study directed by JUM that focused on white sand resident understory
109
bird species. We report the thrushes’ association with white sand forests at our site,
provide the approximate date of first arrival, provide the first confirmation of inter-annual
site fidelity of this species to a non-breeding non-migratory site in South America, and
report the approximate breeding region of 12 individuals (including site-faithful
individuals) inferred via stable isotope (δ2H) analysis. We argue that Gray-cheeked
Thrushes are seasonal residents at our study area. To support our contention, we
compare our capture of Gray-cheeked Thrushes to those of a transient congener –
Swainson’s Thrushes (C. ustulatus) – also captured during this time period. To the best
of our knowledge, our results are the first to report on the occurrence, habitat affiliations,
and migratory connectivity of Gray-cheeked Thrush from a known non-migratory (i.e.,
“wintering”) site in South America. Specifics regarding the range limits and habitat
associations of Gray-cheeked Thrush in South America are lacking (Lowther et al.
2001); therefore, our results are an important contribution toward an understanding of
this species’ life history and conservation needs in the tropics.
Methods
Study Site
Our study site was the Allpahuayo-Mishana National Reserve in the Department
of Loreto, northeastern Peru (-3.922904, -73.516645; Fig. 5-1). The study area is
accessible from the Iquitos-Nauta highway, a paved road on the southern border of the
reserve, or the Nanay River along the northern border.
Field Sampling
The primary objective of the fieldwork was to assess the understory bird
community composition in white sand forests and adjacent clay soil terra firme sites. We
used constant-effort mist-netting to survey understory birds in the reserve from June to
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8 December 2010-2012. Net transects were established in closed-canopy white sand
forest patches that were accessible on foot (elevation: mean = 126.5m, range = 91.9 -
165.9m). Some nets were simultaneously placed in adjacent closed-canopy alluvial
terra firme clay based forest (elevation: mean = 116.7 m, range = 106.1 – 130.6 m). All
nets were 38-mm mesh, 12 m x 3 m, tethered with five trammels. We created transects
through the forest interior using a line of 20 nets set on existing foot trails in primary
forest or older secondary growth forest contiguous with primary forest. Nets were
separated from one another by <1 m when possible. The minimum distance between
transect sites was 250 m. The greatest distance between any two transects was 18 km
(Fig. 5-1). Each net transect was opened for ~500 net hrs during three consecutive
days. We opened nets by 06:00 and checked nets approximately every 30 min until
16:00 on the first two days and 11:00 on the third day. Thrushes were weighed, aged
using criteria in Pyle (2008), and subcutaneous fat scores (0 = none to 7 = very
excessive) were recorded using the MAPS protocol (DeSante et al. 2015). A single
rectrix was collected from Gray-cheeked Thrushes. After three days, we relocated each
transect; no site was sampled for more than three days in a given year.
We used the earliest first arrival date of Gray-cheeked Thrushes (13 October) to
calculate sampling effort for thrushes in the two forest types over the three seasons.
Nineteen transects were sampled each year during the period 13 October to 8
December with a total of ~20,000 net hours in white sand forest and ~3000 net hours in
clay forest. To test for a difference in capture rate between clay and white sand forests,
we used a Chi-square Goodness-of-fit test adjusted for sample effort. Student’s t-test
was used to compare the mean fat scores of Gray-cheeked Thrushes to the congener
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Swainson’s Thrush (C. ustulatus). All statistical tests were accomplished using the R
programming language version 3.0.1.
Stable Isotope Analysis
Feathers of Gray-cheeked Thrushes were sampled (N = 12) in 2012 after we
realized the opportunity to conduct stable isotope analyses posed by the capture of
numerous thrushes at our study site. A single rectrix was collected from individuals at
the time of capture and preserved in separate paper envelopes for future stable isotope
analysis. A 2:1 chloroform:methanol solvent rinse was used to remove surface oils.
Samples were prepared for stable-hydrogen isotope analysis and analyzed at the
Stable Isotope Hydrology and Ecology Laboratory of Environment Canada in
Saskatoon, Canada. Stable-hydrogen isotope analyses of feathers were conducted
using the comparative equilibration method (Wassenaar and Hobson 2001) through
use of calibrated keratin δ2H reference materials. Stable-hydrogen isotope
measurements were performed on H2 derived from high-temperature (1350˚C) flash
pyrolysis of 350 ± 10 ug feather subsamples using continuous-flow isotope-ratio mass
spectrometry. Measurement of three keratin laboratory reference materials (CBS, SPK,
and KHS; corrected for linear instrumental drift) were both accurate and precise, with
typical mean δ2H values of –197, -121, and -54.1‰, respectively. A control keratin
reference yielded a 6-month SD of ± 3.3‰ (N = 76) and within-run SD of standards
typically < 2‰. All results are for non-exchangeable δ2H expressed in the typical delta
notation, units per mil (‰), and normalized on the Vienna Standard Mean Ocean Water
– Standard Light Antarctic Precipitation (VSMOW-SLAP) standard scale.
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Isotopic Assignment
Based on feather δ2H values (δ2Hf), our assignment to origin followed the
procedure described in Hobson et al. (Hobson et al. 2014). Briefly, our δ2Hf isoscape
was calibrated from the growing-season precipitation (δ2Hp) surface created by Bowen
et al. (Bowen et al. 2005) using the ground foraging Neotropical migrant algorithm δ2Hf
= −17.57 +0.95*δ2Hp. The probability of occurrence of a feather with a measured δ2Hf
value at a given pixel was assessed using a likelihood-based assignment method that
was confined to the breeding range in North America of Gray-cheeked Thrush.
Following the derivation of probability of origin at each pixel for each individual, a 2:1
odds ratio was applied as a criteria for including each pixel or not when summing up
surfaces for all individuals in the population. The resulting surface represents the
number of individuals so assigned for each pixel within the delimited range. All
assignments were conducted in the R statistical computing environment (R Core Team
2016).
Results
From 2010 through 2012, we captured 62 Gray-cheeked Thrushes in both clay
and white sand forests (2010: N = 24; 2011: N = 22; 2012: N = 16), with 57 (92%)
captured in white sand forests and five birds (8%) in adjacent clay forests. Excluding all
recaptures, 43% (N = 27) were aged after-hatch year, 27% (N = 17) were hatch year
birds and 29% (N = 18) were unknown (Fig. 5-2). During the same time period, we
caught 22 Swainson’s Thrushes in both clay and white sand forests (2010: N=6; 2011:
N=7; 2012: N=6), with 20 (91%) captured in white sands and two in clay forests (9%;
Fig. 5-3). Controlling for effort, we found no difference in capture rate in white sand
forests and adjacent clay terra firme forests for Gray-cheeked Thrush (χ21 =1.3, P =
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0.24) or Swainson’s Thrush (χ21 =0.29, P = 0.58; both species combined: χ2
1 =1.6, P =
0.20). Over the three-year period, the earliest capture of Gray-cheeked Thrushes in
white sand forest was 13 October and the earliest capture in clay forest was 1
December; the earliest capture of Swainson’s Thrushes in white sands was 11 October
with the earliest capture in clay forests 28 October. The latest capture of Gray-cheeked
Thrushes in either habitat was 7 December at the end of our sampling period (8
December). The latest capture of Swainson’s Thrushes was 16 November (Fig. 5-3).
Capture data, including latitude and longitude for individual Gray-cheeked Thrushes, are
reported in Supplemental Table S1.
We recaptured 12 Gray-cheeked Thrushes (19.3%) on at least one subsequent
day from that in which they were banded including three recaptures in a subsequent
year (4.8%). Dates of recapture occurred from 19 October 2011 to 6 December 2012
(Fig. 5-4). Inter-annual recaptures occurred on 19 October and 21 November 2011, and
17 November 2012. We recaptured two individual Swainson’s Thrushes on subsequent
days (9%) and none in a subsequent year. Because net transects were relocated every
three days, all intra-annual recaptures of Gray-cheeked Thrushes (N = 9) occurred
within 2 days of initial capture from 20 October to 6 December. The two Swainson’s
Thrushes were recaptured on 19 - 20 October and 14 - 15 - 16 November (one
individual). All inter-annual recaptures of Gray-cheeked Thrushes occurred at the same
site (net transect) where the individual was first captured and within 5 calendar days in
subsequent years. One bird initially captured in 2010 was recaptured in 2012 on the
same calendar date and at the same sampling location. All recaptures for both thrush
species were in white sand forests. Stable hydrogen isotope values (δ2H) ranged from -
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174 to -129 ‰ (mean = -161 ‰). Based on our limited δ2H analyses, all birds tested
were from breeding locations in northwest Canada or Alaska rather than north-central or
eastern North America (Fig. 5-5). Gray-cheeked Thrush subcutaneous fat scores were
significantly lower than Swainson’s Thrush subcutaneous fat scores (Gray-cheeked
Thrush: mean=0.54, range = 0 – 4; Swainson’s Thrush: mean = 1.4, range = 0 – 5; P =
0.02).
Discussion
We confirmed inter-annual fidelity of Gray-cheeked Thrushes to sites in white
sand forests in the western Amazon basin. Although Veeries (C. fuscescens) and
Swainson’s Thrushes (C. ustulatus) have been tracked for full annual cycles (Heckscher
et al. 2011, 2015, Hobson and Kardynal 2015, Cormier et al. 2013), geolocator data do
not have the resolution necessary to determine if individuals returning to regions in a
subsequent year are faithful to a former site. Therefore, we believe our study is also the
first to confirm inter-annual site fidelity of migratory Catharus thrushes to a wintering site
in South America. However, non-breeding season site philopatry has been reported for
Bicknell’s Thrushes (C. bicknelli) in the Dominican Republic (Rimmer and McFarland
2001) and Hermit Thrushes (C. guttatus) in the southeast United States (Brown et al.
2000). Of 271 settled Hermit Thrushes banded at their Louisiana USA wintering site,
Brown et al. (2000) reported that 25 (18%) were known to have returned in a
subsequent year of which 23 (98%) were captured within 30 m of the same net in which
they were originally banded (Brown et al. 2000). Similarly, all three of our inter-annual
recaptures occurred at the same transect and within 90 m of where the bird was banded
in a prior year.
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Although some of the 62 Gray-cheeked Thrushes captured in our study (Table 5-
1) may represent transient birds, we assume many of our captures represent birds that
are seasonally settled because: (1) a 4.8% inter-annual recapture rate, all occurring at
the same transect and ≤90m from initial capture, would be extraordinary at a migratory
stopover site (we believe our inter-annual recapture rate would have been higher had
transects remained stationary for more than three days), (2) dates of first capture and
recaptures (13 October – 6 December) were consistent with the timing of arrival and
settlement of Veeries at their winter sites in South America, a Nearctic-Neotropical
migratory congener that also migrates to the Amazon basin from North America, (15
October – 8 December; (Heckscher et al. 2015)), (3) our study site was at or near the
southern edge of the species’ known South American range (Lowther et al. 2001), (4)
the pattern of capture of Gray-cheeked Thrushes is inconsistent with birds captured at a
migratory stopover site. The temporal pattern of transients captured on stopover
typically show a unimodal distribution similar to our Swainson’s Thrush captures (Fig. 5-
2) and shown by Gómez et al. (2014) for three Catharus species at a Colombia
stopover site (Gomez et al. 2014), (5) Gray-cheeked Thrush had significantly lower fat
scores than transient Swainson’s Thrush. Birds recently arrived or settled on winter
sites are expected to have largely exhausted their migratory fat stores while birds en
route at stopover should retain more fat, and (6) a 19% intra-annual recapture rate from
sites re-sampled no more than two days in a given year would be high in comparison to
other work at migratory stopover sites and suggests the presence of settled and
perhaps territorial birds. For example, using 10 – 15 12m nets over a three year period,
Gómez et al. 2014 reported recapture rates between 1.5% and 20% for three species of
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Catharus migrants at two different stopover regions in Colombia (Gomez et al. 2014).
However, their nets remained stationary from mid-September to mid-November and
many recaptures occurred >2 days after initial capture. In contrast, we assume
Swainson’s Thrush used our study area as a migratory stopover site considering: (1)
we have no records of inter-annual recaptures, (2) dates of first capture and recaptures
are not consistent with sedentary birds (11 October – 16 of November), (3) the unimodal
pattern of capture for Swainson’s Thrushes at our sites is typical for a transient
songbird, (4) intra-annual capture rates (9%) were lower than that for Gray-cheeked
Thrush and more typical for birds on stopover (Gomez et al. 2014).
Nearctic-Neotropical migratory Catharus thrushes can move substantial
distances through tropical regions throughout much of the non-breeding season,
particularly in the vast rainforests of South America (Heckscher et al. 2015). To date,
individual Gray-cheeked Thrushes have not been tracked to determine if they exhibit
mid-season movements. The possibility that Gray-cheeked Thrushes undertake mid-
season intra-tropical movements means they may not remain in white sand forests of
the western Amazon for the entire non-breeding season. Regardless, our findings show
that despite two annual trans-hemispheric migrations, Gray-cheeked Thrushes can
show inter-annual site fidelity to specific wintering sites as they probably do for breeding
sites in boreal forest. Site fidelity presumably has selective advantages if birds returning
to familiar sites can use that familiarity to their advantage, e.g., increased foraging
efficiency due to prior knowledge of productive areas, effective evasion of predators, or
intraspecific dominance in high-quality habitat (Wunderle and Latta 2000). Site fidelity
also has implications for conservation. Species in which individuals make annual returns
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to the same site may be more vulnerable to site degradation such as habitat loss
(Warkentin 1996).
We only documented site fidelity in white sand forests, but Gray-cheeked
Thrushes were also captured in adjacent alluvial clay terra firme forests. Controlling for
effort, there was no difference in capture rates between white sand and clay forests,
but, over the three-year period, three of our five captures in clay forest occurred on one
day suggesting possible synchronous movement. Outside this day (4 December 2011),
our capture rates were higher in white sand forests (χ2=4.8, df = 1, P = 0.02). This
difference suggests that Gray-cheeked Thrushes may favor white sand forests in this
region (e.g., late arriving individuals may settle in less desirable clay forests). Thus,
although we assume that Gray-cheeked Thrushes are not solely dependent on white
sand forests for non-breeding sites given their broad distribution in South America, we
conclude that they are associated with white sand forests in this region and may favor
them over adjacent clay terra firme forest. Sexual habitat segregation may be a
contributing factor to differences in habitat use. For example, Bicknell’s Thrushes may
segregate habitat whereby males are more common than females in sites with denser
understory vegetation (Townsend et al. 2011). Unfortunately, we were not able to sex
our birds but later arrivals in clay forests may differ in age or sex; for example, late
arrivals might be young females following a pattern seen in Veery (Heckscher, unpub.
data). In our study area white sand forests do differ structurally and floristically from clay
forest: they have a higher number of vertical stems (<10 cm DBH) in the understory (<3
m) and therefore maintain a denser shrub-layer as opposed to clay forests that have
more herbaceous vegetation and an open understory (see Table 3-1). Thus, Gray-
118
cheeked Thrushes may favor the comparatively dense understory of white sand forest
on their wintering grounds similar to their affiliation with dense vegetation on their
breeding grounds (Lowther et al. 2001, Marshall 2001).
Based on our δ2H analyses, all Gray-cheeked Thrushes were from breeding
locations in northwest Canada or Alaska. These results provide the first data regarding
the connectivity between boreal and tropical forest populations of Gray-cheeked Thrush.
Although our sample size is small, it seems that the connectivity of Gray-cheeked
Thrush between breeding and wintering grounds may be strong, as it is for Swainson’s
Thrushes (Cormier et al. 2013), rather than weak as it is for Veery (Heckscher et al.
2011, Hobson and Kardynal 2015).
Considering that this species winters across a broad east-west expanse of
northern South America (Lowther et al. 2001), one might assume that birds that breed in
western North America might winter in western South American regions as that pattern
represents the shortest distance between breeding and non-breeding regions. However,
songbirds do not always take the simplest possible migratory routes (Ruegg 2002).
Further, it has become apparent that Veeries breeding in eastern and western North
America occur in sympatry in Amazonia (Heckscher et al. 2011, Hobson and Kardynal
2015). Further, birds at our study site may relocate substantial distances long before
they initiate Nearctic-Neotropical migration in April. For example, Veeries in the Amazon
river basin may move >2000 km between first and second winter sites (Heckscher et al.
2015). Annual tracking of individual Gray-cheeked Thrushes is needed to more fully
assess regional patterns of settlement and movement and the connectivity between
breeding and wintering regions.
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Our results suggest that Gray-cheeked Thrushes exhibit site fidelity and may
concentrate in white sand forests – an uncommon and scattered ecosystem type in
western Amazonia. We have also established the connectivity of Gray-cheeked
Thrushes at a specific non-migratory site in South America with breeding regions in
northwestern North America. Further study is needed to better establish migratory
connectivity, temporal patterns of movement, the dependency of this species on white
sand forests, and the use of weathered clay terra firme and lowland forests in the
western Amazon basin. White sand forests are rare in Amazonia and the affiliation of
Gray-cheeked Thrush with this ecosystem type provides new and valuable information
about this ecosystem and the distribution and ecology of Gray-cheeked Thrushes in
South America.
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Table 5-1. Captured and recaptured Gray-cheeked Thrushes (Catharus minimus) in Amazonian white sand forests (wsf) and clay terra firme forests (clay) in Allpahuayo-Mishana National Reserve, Department of Loreto, Peru (2010 – 2012).
Band
Number Date Habitat Net Line and Year Longitude Latitude
Elevation
(m)
E00232 13-Oct-10 wsf Var Fun Piu_2010 -73.4443 -3.9879 154.7
E00241 20-Oct-10 wsf San Mar 1_2010 -73.4410 -3.8868 128.9
E00244 20-Oct-10 wsf San Mar 1_2010 -73.4411 -3.8881 121.3
E00262 23-Oct-10 wsf San Mar 2_2010 -73.4434 -3.8905 117.6
E00261 23-Oct-10 wsf San Mar 2_2010 -73.4433 -3.8905 110.9
E00261* 23-Oct-10 wsf San Mar 2_2010 -73.4438 -3.8903 117.2
E00266 24-Oct-10 wsf San Mar 2_2010 -73.4433 -3.8905 110.9
E00266* 24-Oct-10 wsf San Mar 2_2010 -73.4424 -3.8908 120.5
E00293 4-Nov-10 wsf Var Chico_2010 -73.4755 -3.8955 114.8
E00302 5-Nov-10 wsf Var Chico_2010 -73.4755 -3.8955 114.8
E00324 7-Nov-10 wsf Var Madre_2010 -73.4775 -3.9131 129.9
E00313 7-Nov-10 wsf Var Madre_2010 -73.4776 -3.9132 129.7
E00317 7-Nov-10 wsf Var Madre_2010 -73.4777 -3.9134 129.7
E00324* 8-Nov-10 wsf Var Madre_2010 -73.4774 -3.9128 131.8
E00327 8-Nov-10 wsf Var Madre_2010 -73.4776 -3.9133 130.1
E00313* 8-Nov-10 wsf Var Madre_2010 -73.4777 -3.9134 129.7
E00324* 9-Nov-10 wsf Var Madre_2010 -73.4774 -3.9127 127.7
E00347 17-Nov-10 wsf Var Yuto 1_2010 -73.5342 -3.9113 166.0
E00360 18-Nov-10 wsf Var Yuto 1_2010 -73.5350 -3.9122 136.9
E00365 19-Nov-10 wsf Var Yuto 1_2010 -73.5342 -3.9113 166.0
* re-captured individuals, ** Inter-annual recapture.
121
Table 5-1. Continued Band
Number Date Habitat Net Line and Year Longitude Latitude
Elevation
(m)
E00374* 20-Nov-10 wsf Var Yuto 2_2010 -73.5297 -3.9105 142.2
E00366 20-Nov-10 wsf Var Yuto 2_2010 -73.5315 -3.9115 144.3
E00374* 20-Nov-10 wsf Var Yuto 2_2010 -73.5310 -3.9109 117.2
E00370* 20-Nov-10 wsf Var Yuto 2_2010 -73.5308 -3.9108 139.3
E00370* 21-Nov-10 wsf Var Yuto 2_2010 -73.5299 -3.9106 127.5
E00406 25-Nov-10 wsf Var Yuto 3_2010 -73.5364 -3.9180 79.4
E00420 26-Nov-10 wsf Var Yuto 3_2010 -73.5366 -3.9163 133.0
E00437 3-Dec-10 wsf Var Exp Porv_2010 -73.5534 -3.9192 115.2
E00447 5-Dec-10 wsf Var Blanco_2010 -73.5586 -3.9159 116.2
E00453 6-Dec-10 wsf Var Blanco_2010 -73.5591 -3.9154 122.2
E00447* 6-Dec-10 wsf Var Blanco_2010 -73.5590 -3.9156 127.0
E00455 7-Dec-10 wsf Var Blanco_2010 -73.5583 -3.9160 127.5
E00838 19-Oct-11 wsf San Mar 1_2011 -73.4411 -3.8873 121.3
E00244** 19-Oct-11 wsf San Mar 1_2011 -73.4411 -3.8871 119.8
E00915 19-Oct-11 wsf San Mar 2_2011 -73.4424 -3.8908 120.5
E00838* 20-Oct-11 wsf San Mar 1_2011 -73.4411 -3.8870 121.5
E00920 20-Oct-11 wsf San Mar 2_2011 -73.4426 -3.8908 122.5
E00922 20-Oct-11 wsf San Mar 2_2011 -73.4439 -3.8903 112.4
E00863 25-Oct-11 wsf Var Bol 1_2011 -73.4566 -3.8908 116.2
E00960 2-Nov-11 wsf Var Bol 2_2011 -73.4539 -3.8944 110.9
E00720 20-Nov-11 wsf Var Yuto 3_2011 -73.5366 -3.9175 131.1
E00716 20-Nov-11 wsf Var Yuto 3_2011 -73.5363 -3.9180 131.6
* re-captured individuals, ** Inter-annual recapture.
122
Table 5-1. Continued Band
Number Date Habitat Net Line and Year Longitude Latitude
Elevation
(m)
E00741 23-Nov-11 wsf Var Yuto 1_2011 -73.5348 -3.9119 153.2
E00757 27-Nov-11 wsf Var Yuto 2_2011 -73.5310 -3.9110 137.4
E00761 30-Nov-11 wsf Var Exp Porv_2011 -73.5530 -3.9189 135.9
E00773 30-Nov-11 wsf Var Exp Porv_2011 -73.5526 -3.9183 132.1
E00777 1-Dec-11 wsf Var Exp Porv_2011 -73.5524 -3.9182 132.5
E00798 4-Dec-11 wsf Var Blanco_2011 -73.5587 -3.9159 131.3
(no band) 4-Dec-11 wsf Var Blanco_2011 -73.5582 -3.9165 128.5
E00792 4-Dec-11 wsf Var Blanco_2011 -73.5581 -3.9166 129.2
E00989 4-Dec-11 clay Porv TF_2011 -73.5524 -3.9103 113.8
E00793 4-Dec-11 wsf Var Blanco_2011 -73.5591 -3.9154 131.3
E00975 4-Dec-11 clay Porv TF_2011 -73.5522 -3.9115 114.5
E00972 4-Dec-11 clay Porv TF_2011 -73.5522 -3.9114 115.5
E00800 5-Dec-11 wsf Var Blanco_2011 -73.5591 -3.9154 131.3
E00800* 6-Dec-11 wsf Var Blanco_2011 -73.5586 -3.9159 131.3
E1009 6-Dec-11 wsf Var Blanco_2011 -73.5591 -3.9154 131.3
E1238 25-Oct-12 wsf Var Madre_2012 -73.4780 -3.9136 130.4
E1238* 25-Oct-12 wsf Var Madre_2012 -73.4768 -3.9121 121.0
E1242 25-Oct-12 wsf Var Madre_2012 -73.4769 -3.9123 90.5
E1237 25-Oct-12 wsf Var Madre_2012 -73.4771 -3.9125 91.9
J00812 16-Nov-12 wsf Var Yuto 1_2012 -73.5346 -3.9117 139.3
C0497 16-Nov-12 wsf Var Yuto 1_2012 -73.5342 -3.9112 138.1
J00811 16-Nov-12 wsf Var Yuto 1_2012 -73.5340 -3.9112 138.6
* re-captured individuals, ** Inter-annual recapture.
123
Table 5-1. Continued Band
Number Date Habitat Net Line and Year Longitude Latitude
Elevation
(m)
E1534 17-Nov-12 wsf Var Yuto 2_2012 -73.5304 -3.9104 135.2
J00819 19-Nov-12 wsf Var Yuto 3_2012 -73.5365 -3.9161 135.7
J00816 19-Nov-12 wsf Var Yuto 3_2012 -73.5366 -3.9174 129.9
J00817 19-Nov-12 wsf Var Yuto 3_2012 -73.5366 -3.9166 132.5
J00818 19-Nov-12 wsf Var Yuto 3_2012 -73.5366 -3.9168 132.8
J00817* 19-Nov-12 wsf Var Yuto 3_2012 -73.5366 -3.9169 132.8
J00816* 20-Nov-12 wsf Var Yuto 3_2012 -73.5366 -3.9164 136.4
E1545 20-Nov-12 wsf Var Yuto 3_2012 -73.5366 -3.9168 132.8
E1567 30-Nov-12 wsf Var Exp Porv_2012 -73.5526 -3.9183 122.2
E1576 1-Dec-12 clay Porv TF_2012 -73.5522 -3.9112 115.0
J00838 4-Dec-12 wsf Var Blanco_2012 -73.5594 -3.9150 126.1
J00838* 6-Dec-12 wsf Var Blanco_2012 -73.5587 -3.9158 129.2
* re-captured individuals, ** Inter-annual recapture.
124
Figure 5-1. Location of Allpahuayo-Mishana National Reserve (outline), Department of
Loreto, southeast of Iquitos, Peru. White sand forests are the small polygons outlined in black. Locations of mistnet transects in white sand are depicted as white triangles and those in clay terra firme forests as black circles. Inset in lower left corner is the outline of Peru with the Department of Loreto in black with the approximate location of the study sites depicted by a star (image source: Google Earth).
125
Figure 5-2. Weekly numbers of Gray-cheeked Thrushes by age caught using constant
effort mistnet transects at Allpahuayo-Mishana National Reserve, Department of Loreto, Peru, 13 October 2010 to 8 December 2012.
0
1
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3
4
5
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9HY
AHY
Unknown
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126
Figure 5-3. Cumulative number of Gray-cheeked Thrushes (Catharus minimus) and Swainson’s Thrushes (C. ustulatus) captured over a nine week period in 2010, 2011, and 2012 at Allpahuayo-Mishana National Reserve, Department of Loreto, Peru. Numbers over columns indicate the number of individuals captured in weathered clay terra firme forests, all others were captured in white sand terra firme forests.
0
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12
14
16
18
Gray-cheeked Thrush
Swainson's Thrush
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127
Figure 5-4. Cumulative recaptures by week for Gray-cheeked Thrushes using constant effort mistnet transects at Allpahuayo-Mishana National Reserve, Department of Loreto, Peru. Transects were relocated every three days, such that each transect was sampled for only three days each year. Numbers above columns indicate inter-annual recaptures for the respective weekly totals.
0
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um
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128
Figure 5-5. Depictions of likely breeding or natal origins of Gray-cheeked Thrushes at Allpahuayo-Mishana National Reserve, Department of Loreto, Peru. The spatially explicit assignment of the population (n=12) follows from the likelihood assignment approach described in Hobson et al. (2014). Numbers in the legend refer to number of individuals in the sample whose probability of assignment, based on a 2:1 odds ratio, at each pixel was consistent with the expected feather value for that location. Possible breeding or natal origin was delimited by the known breeding range of Gray-cheeked Thrush in North America.
129
CHAPTER 6 CONCLUDING REMARKS
Tropical rainforests harbor the most species-rich communities on Earth. Many
groups of organisms reach the highest levels of species richness and largest biomass in
Neotropical rainforests, and ecological research is often focused on elucidating the
underlying mechanisms and the consequences of diversity (Gaston 2000, Storch et al.
2006). Beta diversity, or turnover between localities, in our analyses between habitats,
contributes to the high levels of species richness in the tropics (Condit et al. 2002,
Jankowski et al. 2009, Kraft et al. 2011), and the high turnover in taxonomic and
phylogenetic betadiversity is governed by various mechanisms. In this dissertation we
combined a phylogenetic approach with the observed ecological patterns on a large
geographic scale, and then focused on field data collection on a smaller spatial scale to
observe community patterns in contrasting habitat types. Floristic studies have been
more commonly used to examine this ecosystem (Fine et al. 2010, ter Steege et al.
2013, Coronado et al. 2015), but the first-ever Amazon-basin wide description of
betadiversity and phylobetadiversity study, focusing on white sand systems’ plants was
published just this year (Guevara et al. 2016). The study found high taxonomic
dissimilarity but low phylogenetic dissimilarity in pairwise community comparison.
Many questions remain regarding the diversity that white sand forests harbor, the
processes that govern community composition, structure and the basic biogeographical
patterns of this ecosystem. Amazonian white sand systems function as ecological
islands in the sea of the tropical moist forest biome; endemic plants are abundant and
various habitat specialists show disjoint ranges that are specific to this particular habitat
type (Fine 2016, Fine and Baraloto 2016). Plant community assemblages strongly
130
correlate with differences in soil (Poulsen et al. 2006). In Western Amazonia, on the
gradient of soil types from nutrient poor sandy soils to relatively fertile clay soils, most
plants, especially terrestrial pteridophytes, some palms, and some species of ferns, are
restricted to specific soil conditions, and only a few can grow on all soil types (Tuomisto
and Poulsen 1996, Svenning 2001, Fine et al. 2005, Svenning et al. 2006, Fine et al.
2010).
Despite differences in plant communities in the different forest types, we did not
find strong evidence of fundamentally different avian community composition due to
habitat filtering, nor did we find population structure in an obligate habitat specialist
species. There is much to explore in the system that still requires further research.
Habitat specialization does not seem to be an isolating mechanism in most groups of
taxa. For example, within plants, only ~23% of WSF forest plants from Western
Amazonia are actually habitat specialists (Garcia-Villacorta et al. 2016). This study did
not explore the contribution of interspecific contest competition of habitat segregation
and how that plays a role in beta diversity, even though we initially predicted that the
proliferation of birds on different habitats would be governed by competition between
closely related species.
Natural communities vary on a scale from local dynamics to regional species
distribution patterns. Studies of white sands have explored the vegetation community,
but no large-scale ecological studies have been conducted on their bird communities.
There are no concise analyses of the resource and habitat use of white sand forest
specialist birds, and no data on their potential dispersal ability. There have been no
population genetics studies on isolated populations of the specialist species to
131
determine whether gene flow exists between the patches, but there is a study in
progress (using samples collected from our fieldwork) at the Field Museum focusing on
the phylogeography of poor soil specialist bird species all across their distributional
ranges (Capurucho, Joao Marcos, pers. comm.).
If we compare species lists from studies in Loreto (North bank of the Amazon in
Peru) with studies from Jau National Park (North bank of the Amazon in Brazil) and in
the upper Jurua River region in Acre (South bank of the Amazon in Brazil), there is
surprisingly high overlap between the species inhabiting these stunted forests (Alonso
and Whitney 2003, Borges 2004, Guilherme and Borges 2011), indicating that perhaps
at some point they may have been more widespread or connected by dispersal
corridors. An integrative, well-designed approach emphasizing the number and spatial
distribution of samples combined possibly with long-term manipulative experiments
would allow us to evaluate macroevolutionary and biogeographic processes (Urban et
al. 2008), and to make predictions of changes and consequences relevant for
conservation biology.
Long-term studies also allow for the study of cryptic or poorly known species.
During the constant effort mist netting and mark-recapture study, we sampled a WSF
habitat specialist in high enough numbers to be able to conduct a population genetics
study. We collected blood samples, some of which are serving as the first tissue
samples of species from this region, and are already being used by collaborators for
future studies on the biogeography of Amazonian habitat specialists. The blood samples
are also being used in an avian malaria study (Pulgarin-R, P. pers. comm). In addition,
we captured 62 Catharus minimus and 17 Catharus ustulatus individuals throughout our
132
fieldwork, and apparently, this is the greatest number of C. minimus reported from any
single South American site (Ungvari-Martin et al. 2016) and may exceed the number of
published accounts from all of South America. White sand forests are proving to
become an important site migratory bird species as well as residents.
Long periods spent in the field can yield data for various future publications. Our
dataset, not included in this dissertation, will allow us to compare age structure, survival
estimates, morphological differences, and even further detailed vegetation dynamics in
the future. In addition, we also collected a large number of ectoparasites and fecal
samples that can serve for co-evolutionary studies on host-parasite interactions using
lice and their avian hosts, and the fecal samples can be used for diet studies. Overall,
the four content chapters of this dissertation barely start the exploration of all the
information collected from AMNR, and we hope to contribute with these data for further
publications in years to come.
There is no one correct way to do ecology. Mathematical models, model ecosystems, field manipulation experiments and the search for large-scale patterns are all valid approaches, and all have their strengths and weaknesses. They are simply tools to help us understand nature; like all tools, each approach does some things well, some things, badly, and other things not at all (Lawton 1996).
133
APPENDIX A LIST OF SPECIES CAPTURED IN AMNR FORESTS
Table A-1. List of species captured in forest in Allpahuayo-Mishana during 2009-2012 with raw capture numbers for each species
Species terra firme
humid varillal dry varillal chamizal
Glyphorynchus spirurus 239 515 725 87
Dixiphia pipra 104 228 290 42
Willisornis poecilonotus 105 158 260 26
Pithys albifrons 90 111 230 25
Gymnopithys leucaspis 81 137 216 18
Dendrocincla merula 19 65 157 15
Mionectes oleagineus 106 155 118 19
Neopelma chrysocephalum 0 63 116 12
Xiphorhynchus elegans 28 38 107 12
Myrmotherula axillaris 40 75 89 14
Myrmotherula hauxwelli 62 46 88 2
Phaetornis bourcieri 43 44 82 3
Megastictus margaritatus 31 54 81 8
Phaetornis superciliosus 62 33 66 9
Automolus ochrolaemus 7 50 51 12
Schiffornis turdina 4 21 49 8
Dendrocincla fuliginosa 11 30 48 7
Myrmoborus myotherinus 86 14 46 0
Catharus minimus 4 33 42 0
Lepidothrix coronata 118 28 41 2
Pipra erythrocephala 45 12 38 3
Thalurania furcata 8 21 35 0
Corythopis torquatus 4 36 33 1
Xenops minutus 25 15 32 4
Malacoptila fusca 7 2 31 0
Sclerurus rufigularis 7 13 31 0
Formicarius colma 3 14 30 6
Percnostola arenarum 1 17 25 5
Geotrygon montana 13 10 22 1
Threnetes leucurus 9 4 22 1
Epinecrophylla haematonota 28 37 21 1
Hylophylax naevius 10 19 21 0
Microcerculus marginatus 24 19 21 0
Myrmeciza castanea 3 10 20 5
134
Table A-1. Continued
Species terra firme
humid varillal dry varillal chamizal
Thamnomanes caesius 35 18 19 0
Catharus ustulatus 2 9 18 0
Terenotriccus erythrurus 29 11 17 0
Cnemotriccus spp. 0 4 14 8
Sclerurus mexicanus 12 14 14 1
Heliodoxa aurescens 4 2 13 0
Phlegopsis erythroptera 20 5 12 0
Machaeropterus regulus 24 5 11 0
Synallaxis rutilans 23 23 11 0
Attila spadiceus 6 6 10 1
Dendrocolaptes certhia 2 2 10 0
Thamnophilus murinus 8 7 10 1
Chloroceryle aenea 5 7 9 0
Platyrinchus saturatus 4 0 9 0
Turdus albicollis 4 7 9 0
Deconychura stictolaema 3 2 8 0
Ramphotrigon ruficauda 3 3 8 0
Myrmeciza fortis 26 4 7 0
Neopipo cinnamomea 1 2 7 0
Nonnula brunnea 27 6 7 5
Heterocercus aurantiivertex 0 1 6 4
Manacus manacus 31 15 6 3
Rhegmatorhina melanosticta 7 7 6 0
Florisuga mellivora 2 3 5 1
Frederickena unduligera 4 3 5 0
Glaucis hirsutus 1 2 4 1
Hylocharis cyanea 0 4 4 0
Onychorhynchus coronatus 2 3 4 1
Attila citriniventris 0 3 3 0
Rupornis magnirostris 1 0 3 2
Celeus elegans 2 2 3 4
Laniocera hypopyrra 1 1 3 0
Rhytipterna simplex 0 2 3 0
Veniliornis affinis 1 0 3 1
Capito auratus 3 1 2 1
Caprimulgus nigrescens 0 0 2 0
Cercomacra serva 3 0 2 0
135
Table A-1. Continued
Species terra firme
humid varillal dry varillal chamizal
Chrysuronia oenone 0 0 2 0
Conopophaga peruviana 11 3 2 0
Cyanocompsa cyanoides 9 0 2 0
Galbula albirostris 9 1 2 0
Gymnopithys lunulatus 1 0 2 0
Hyloctistes subulatus 19 4 2 0
Hypocnemis hypoxantha 6 12 2 3
Lophotriccus vitiosus 0 0 2 0
Momotus momota 2 3 2 1
Sporophila angolensis 1 1 2 0
Pachyramphus marginatus 0 0 2 0
Phaeothlypis fulvicauda 0 1 2 1
Sclateria naevia 1 1 2 0
Tangara chilensis 1 1 2 0
Accipiter superciliosus 0 0 1 0
Ancistrops strigilatus 0 0 1 0
Celeus grammicus 0 0 1 2
Chlorostilbon mellisugus 0 2 1 0
Claravis pretiosa 0 1 1 1
Cyanerpes caeruleus 0 0 1 0
Deconycura longicauda 0 1 1 0
Hylocharis sapphirina 0 0 1 0
Micromonacha lanceolata 0 0 1 0
Microrhopias quixensis 0 0 1 0
Myrmotherula menestriesii 2 1 1 0
Neoctantes niger 0 1 1 0
Notarchus ordii 0 0 1 1
Oporornis agilis 0 0 1 0
Percnostola leucostigma 17 10 1 3
Philydor erythrocercum 0 2 1 0
Phoenicircus nigricollis 0 0 1 1
Thamnophilus doliatus 0 0 1 0
Thryothorus coraya 7 1 1 0
Trogon rufus 0 0 1 0
Automolus infuscatus 1 0 0 0
Bucco tamatia 1 0 0 0
136
Table A-1. Continued
Species terra firme
humid varillal dry varillal chamizal
Cacicus cela 0 0 0 2
Cnipodectes subbrunneus 13 4 0 0
Crypturellus soui 1 0 0 0
Cyanerpes cyaneus 0 1 0 0
Electron platyrynchus 1 0 0 0
Euphonia rufiventris 1 1 0 0
Euphonia xanthogaster 5 0 0 0
Habia rubica 2 3 0 0
Hylophilus ochraceiceps 18 4 0 0
Hypocnemis peruviana 8 0 0 0
Leucopternis melanops 1 0 0 0
Lipaugus vociferans 2 1 0 0
Micrastur gilvicollis 2 2 0 0
Micrastur mirandollei 1 1 0 0
Monasa morphoeus 3 0 0 0
Myiobius barbatus 5 0 0 0
Myrmotherula longipennis 4 1 0 0
Pachyramphus polychopterus 1 0 0 1
Poecilotriccus latirostris 0 1 0 0
Pteroglossus azara 1 0 0 0
Pygiptila stellaris 1 0 0 0
Ramphocaenus melanurus 1 0 0 0
Rhynchocyclus olivaceus 1 0 0 0
Saltator grossus 2 0 0 0
Sclerurus caudatus 0 3 0 0
Selenidera reinwardtii 1 2 0 0
Tachyphonus cristatus 2 3 0 0
Tachyphonus surinamus 3 8 0 0
Thamnomanes ardesiacus 9 7 0 0
Thamnophilus schistaceus 6 0 0 0
Tityra semifasciata 0 0 0 1
Turdus lawrencii 0 0 0 1
Xiphorhynchus guttatus 1 0 0 0
137
A)
B)
Figure A-1. Comparisons of WSF and clay and the four specific habitat types by capture
rate. Neither A) Capture rates between clay and WSF nor B) captures rates among the four specific habitat types in AMNR differed.
138
APPENDIX B SUMMARY OF RICHNESS ESTIMATORS
Table B-1. Incidence-based richness estimators with standard deviations of the four different habitat types in AMNR.
Habitat Species chao chao.se jack1 jack1.se boot boot.se n
clay 106 145.77 18.26 137.82 14.20 120.21 8.37 11
humid varillal 99 121.49 11.04 125.36 11.22 111.46 6.35 11
dry varillal 106 134.38 14.33 132.35 8.25 117.95 4.65 17
chamizal 53 70.36 9.14 71.00 13.11 61.52 6.79 3
Figure B-1. Graphical representation of incidence based richness estimators with
confidence intervals for all sampling localities and habitats combined.
139
APPENDIX C BETWEEN-YEAR RETURN RATES IN WSF AND CLAY FOREST
Table C-1. Comparison of return rates for all species with a minimum of 5 captures. All values represent mean number of individual birds in the habitat types ± standard deviations. Habitat specialists are indicated in the last column.
species N (WSF)
Percent recaptures (WSF) N (clay)
Percent recaptures (clay) SPECIALIST
Dixiphia pipra 15.95 ± 8.88 16.14 ± 13.81 14 ± 11.17 6.92 ± 9.44 WSF Neopelma chrysocephalum 8.23 ± 2.8 28.61 ± 21.49 NA NA WSF Megastictus margaritatus 7 ± 2.69 8.63 ± 11.34 7.5 ± 2.12 16.67 ± 23.57 WSF Percnostola arenarum 6 ± 0 0 ± 0 NA NA WSF Cnemotriccus spp. 5 ± 0 0 ± 0 NA NA WSF Glyphorynchus spirurus 30.05 ± 12.84 27.39 ± 12.92 19 ± 10.86 20.55 ± 11.19 NO Mionectes oleagineus 12 ± 5.13 11.44 ± 11.82 13.75 ± 2.63 5 ± 10 NO Pipra erythrocephala 12 ± 0 0 ± 0 5 ± 0 0 NO
Pithys albifrons 9.38 ± 3.1 17.02 ± 14.92 8.5 ± 0.71 28.47 ± 22.59 NO
Willisornis poecilonotus 9 ± 3.69 28.73 ± 18.82 7.67 ± 3.79 6.67 ± 11.55 NO Myrmoborus myotherinus 9 ± 5.66 17.69 ± 3.26 7.75 ± 2.87 16.07 ± 13.66 NO Thamnomanes caesius 9 ± 0 0 ± 0 5.5 ± 0.71 8.33 ± 11.79 NO Dendrocincla merula 8.55 ± 4.2 24.73 ± 21.99 NA NA NO Gymnopithys leucaspis 8.14 ± 2.91 19.43 ± 27.63 7.33 ± 4.04 26.67 ± 30.55 NO Myrmotherula axillaris 7.55 ± 1.86 9.49 ± 12.01 6.5 ± 2.12 0 NO Xiphorhynchus elegans 6.89 ± 2.71 26.17 ± 17.41 5 ± 0 0 NO Catharus minimus 6.86 ± 1.68 3.63 ± 6.26 NA NA NO Myrmotherula hauxwelli 6.17 ± 1.94 13.89 ± 13.07 8 ± 1 8.33 ± 14.43 NO Automolus ochrolaemus 6 ± 0.63 18.25 ± 22.87 NA NA NO Microcerculus marginatus 6 ± 1.41 10 ± 14.14 NA NA NO
Schiffornis turdina 5.75 ± 1.5 8.13 ± 9.87 NA NA NO
140
Table C-1. Continued
species N (WSF)
Percent recaptures (WSF) N (clay)
Percent recaptures (clay) SPECIALIST
Geotrygon montana 5.5 ± 0.71 0 ± 0 NA NA NO Catharus ustulatus 5 ± 0 0 ± 0 NA NA NO Corythopis torquatus 5 ± 0 20 ± 0 NA NA NO Dendrocincla fuliginosa 5 ± 0 10 ± 14.14 NA NA NO Epinecrophylla haematonota 5 ± 0 0 ± 0 5 ± 0 0 NO Lepidothrix coronata 5 ± 0 12 ± 17.89 10.5 ± 3 9.08 ± 7.2 NO
Malacoptila fusca 5 ± 0 0 ± 0 NA NA NO
Turdus albicollis 5 ± 0 20 ± 0 NA NA NO
Xenops minutus 5 ± 0 20 ± 0 5 ± 0 40 ± NA NO
Synallaxis rutilans NA NA 5 ± 0 20 ± NA clay Machaeropterus regulus NA NA 5 ± 0 0 clay Terenotriccus erythrurus NA NA 5 ± 0 0 clay
141
APPENDIX D SUPPLEMENTARY INFORMATION OF DDRADSEQ ANALYSIS
We used 6 uL volume at 20 ng/uL concentration DNA from each sample, which
were digested using 0.9 uL Cutsmart 10x buffer, 0.28 uL EcoRI enzyme, 0.12 uL MseI
enzyme, 1.7 ul water for 8 hours at 37C in a thermocycler. A ligation step was
performed overnight adding 1 uL EcoR1 [0.5uM], 0.4 ul Cutsmart 10x buffer, 1.3 uL ATP
10um, 0.2 ul T4 DNA ligase enzyme, 0.1 ul water, and 1 ul MseI [10uM]. The 14 uL
samples were sealed and incubated at 16C for 6hours in a thermocycler. Library
construction and successful ligation were verified using a PCR reaction using 8 ul NEB
One-Taq 2x Master Mix, 0.8 ul 10 um premixed forward and reverse Illumina primers,
6.2 ul water, and 1 ul of the restriction/ligation product. This PCR was run following the
protocol: 94C for 2min then 20 cycles of of 94C for 30sec, 60C for 30sec, 68C for
45sec, and holding at 10 C. Amplification was verified on a 1.5% agarose gel using
electrophoresis, resulting in smears of visible DNA fragments in the 50-700 bp range.
We combined 7 ul of restriction/ligation product from every sample in a single
Eppendorf tube, and used the ICBR core genomics facilities to perform concentration of
the product, size selection using Pippin and Bioanalyzer evaluation (Submission ID
134735). In order to test the fractions that we wanted to use, we tested samples at
8,10,12,14 PCR cycles using 3 ul Q5 buffer 5x, 0.3ul DNTP, 1 ul of forward and reverse
primers combined at 5 um concentration, 0.15 ul Q5 Taq, 10.5 ul water, and 1 ul of size
selected sample. We ran the PCR with the following conditions on a thermocycler with a
heated lid: 98C for 30 sec, 8-12 cycles of (98C for 15sec, 60C for 30sec, 72C for
30sec), hold at 10C. While generally lower number of cycles are sufficient, we tested
the size selected samples at 12 cycles. We used fractions 5,6,7,8,9,10 for testing and
142
chose fraction 9+8+7+6 for a total number of 60 tubes (15 each for each fraction for the
4 fractions). Finally, we pooled the PCR products after gel electrophoresis and
requested sequencing.
Table D-1. Run metrics summary from the High throughput NextSeq 500 run of single end 150 bp reads.
Cycles Yield (Gbp)
Aligned (%)
Error rate (%) %≥q30
Total sequences
Sequences ≥q33
151 58.51 21.26 0.54 91.49 378,893,170 316,455,808
143
Table D-2. Raw sequencing information for samples after quality filtering steps from the catalog of RAD loci.
Id Region Unique Stacks
Polymorphic Loci SNPs Found
NECH_V1278 km 25 102201 5138 6543
NECH_V222 km 25 197872 26141 33281
NECH_V238 km 25 202132 28242 35871
NECH_V284 km 25 204567 28197 36231
NECH_12E2 km 28 188577 22119 28161
NECH_12E7 km 28 323328 29917 38232
NECH_3104 km 28 208297 32972 42257
NECH_3122 km 28 170905 18095 22799
NECH_3185 km 28 131250 9849 12308
NECH_3297 km 28 319495 29838 38128
NECH_7395 km 28 208023 30449 38994
NECH_V1315 km 28 201981 26835 34106
NECH_3491 km 31 19173 411 510
NECH_3492 km 31 147042 12336 15582
NECH_7356 km 31 190046 21359 27245
NECH_FP30 km 31 210112 30351 39151
NECH_1M89 Mishana 198712 28185 35959
NECH_2ME29 Mishana 141227 10050 12818
NECH_2ME6 Mishana 50408 1762 2229
NECH_2ME63 Mishana 190878 24558 31429
NECH_3845 Mishana 132826 9074 11467
NECH_3863 Mishana 141378 10971 13905
NECH_3908 Mishana 98588 4858 6136
NECH_3915 Mishana 184009 21889 27718
NECH_CH112 Mishana 185199 20909 26878
NECH_CH60 Mishana 202458 27534 35231
NECH_JM27 Mishana 175384 17742 22686
NECH_M188 Mishana 208023 31554 40292
NECH_M523 Mishana 203909 28073 35942
NECH_M530 Mishana 192394 24024 30409
NECH_ME029 Mishana 201557 28474 36321
NECH_ME030 Mishana 184392 22353 28391
NECH_ME062 Mishana 205801 30736 38978
NECH_ME063 Mishana 148887 12457 15818
NECH_ME079 Mishana 192682 24079 30801
NECH_TN01 Mishana 204922 27564 35154
NECH_TN02 Mishana 163834 15887 19986
144
Table D-2. Continued
Id Region Unique Stacks
Polymorphic Loci SNPs Found
NECH_TN03 Mishana 204990 29644 37949
NECH_TN04 Mishana 187119 19668 25554
NECH_TN05 Mishana 160181 15263 19329
NECH_2NEN1 Nueva Esperanza 187165 28245 35158
NECH_2NEN2 Nueva Esperanza 209569 36127 45688
NECH_3301 Nueva Esperanza 130099 8104 10351
NECH_3317 Nueva Esperanza 75170 3233 4108
NECH_3341 Nueva Esperanza 171456 17620 22127
NECH_3372 Nueva Esperanza 153887 13945 17518
NECH_3399 Nueva Esperanza 159466 14891 18472
NECH_3418 Nueva Esperanza 184639 22403 28663
NECH_3431 Nueva Esperanza 134339 9727 12357
NECH_3434 Nueva Esperanza 196369 25642 32900
NECH_5047 Nueva Esperanza 204438 29085 36958
NECH_5078 Nueva Esperanza 151918 14916 18811
NECH_5097 Nueva Esperanza 182605 20584 26204
NECH_5121 Nueva Esperanza 188289 22880 29227
NECH_5213 Nueva Esperanza 194621 24513 31231
NECH_5297 Nueva Esperanza 157192 13673 17313
NECH_5306 Nueva Esperanza 34777 1069 1340
NECH_CU13 Nueva Esperanza 157618 13691 17383
NECH_CU17 Nueva Esperanza 177242 20764 26219
145
Table D-2. Continued
Id Region Unique Stacks
Polymorphic Loci SNPs Found
NECH_CU2 Nueva Esperanza 177434 25156 32268
NECH_CU22 Nueva Esperanza 176478 19717 25007
NECH_CU48 Nueva Esperanza 198058 26569 33892
NECH_CU6 Nueva Esperanza 188522 23633 29915
NECH_NEU38 Nueva Esperanza 67507 2750 3439
NECH_4346 Porvenir 200620 26537 33821
NECH_4354 Porvenir 151186 11971 15253
NECH_4357 Porvenir 46133 1750 2151
NECH_4361 Porvenir 147811 12196 15527
NECH_4432 Porvenir 186452 22108 27866
NECH_4450 Porvenir 99218 5607 7058
NECH_4535 Porvenir 168390 16194 20492
NECH_6783 Porvenir 188707 18098 23341
NECH_6851 Porvenir 204198 28194 36215
NECH_6863 Porvenir 170691 16696 21247
NECH_6865 Porvenir 210763 31468 40371
NECH_6877 Porvenir 178144 19423 24590
NECH_6884 Porvenir 204545 29427 37591
NECH_6898 Porvenir 201463 27566 35196
NECH_6945 Porvenir 179553 19422 24557
NECH_6989 Porvenir 159510 14172 17988
NECH_BL109 Porvenir 104655 5840 7402
NECH_EX101 Porvenir 40893 1716 2164
NECH_EX31 Porvenir 137387 10925 13673
NECH_EX34 Porvenir 198545 27129 34627
NECH_EX58 Porvenir 201438 27012 34276
NECH_EX66 Porvenir 187701 20918 26645
NECH_1B34 San Martin 246815 26680 33916
NECH_2B10 San Martin 202598 28490 36175
NECH_2SM55 San Martin 180443 21892 27818
NECH_2SM73 San Martin 204547 29213 37037
NECH_3636 San Martin 193724 24912 31614
NECH_3654 San Martin 182288 20443 26013
146
Table D-2. Continued
Id Region Unique Stacks
Polymorphic Loci SNPs Found
NECH_3673 San Martin 155924 12944 16476
NECH_3674 San Martin 180701 19873 25303
NECH_3675 San Martin 110075 6569 8419
NECH_3770 San Martin 112617 7363 9389
NECH_3773 San Martin 69955 3674 4553
NECH_6076 San Martin 211390 33534 42927
NECH_6092 San Martin 54400 2043 2673
NECH_VB1 San Martin 180036 19765 25201
NECH_VB2-1 San Martin 193460 24430 30865
NECH_VB2 San Martin 202649 28238 35966
NECH_1Y27 Yuto 207115 29348 37526
NECH_1Y60 Yuto 218711 39051 49685
NECH_1Y63 Yuto 193733 25073 31640
NECH_1Y64 Yuto 203044 28542 36643
NECH_1Y87 Yuto 176730 18388 23376
NECH_1Y89 Yuto 189407 23132 29445
NECH_2Y16 Yuto 193945 25179 32064
NECH_2Y22 Yuto 89269 4241 5458
NECH_2Y70 Yuto 217892 40454 51362
NECH_2Y71 Yuto 218083 40979 52197
NECH_2Y81 Yuto 182097 24177 30347
NECH_3982 Yuto 195076 24054 30633
NECH_3Y40 Yuto 215553 36340 46432
NECH_3Y69 Yuto 218965 42140 53918
NECH_4095 Yuto 205869 28582 36598
NECH_4123 Yuto 164258 15907 20093
NECH_4239 Yuto 202602 28869 36813
NECH_4291 Yuto 162226 15352 19270
NECH_4298 Yuto 165214 15439 19552
NECH_4319 Yuto 181564 21334 26998
NECH_4321 Yuto 119800 7467 9543
NECH_6562 Yuto 169024 16722 21268
NECH_6605 Yuto 205638 29902 38048
NECH_6680 Yuto 176205 20289 25722
147
Table D-3. Summary statistics for the 129 individuals included in the final filtered dataset, 2240 biallelic loci included in the dataset.
Statistic Value SD* c.i.2.5%** c.i.97.5%** Description
Num 2 0 2 2 Number of alleles
Eff_num 1.412 0.005 1.402 1.422 Effective number of alleles
Hs 0.331 0.003 0.325 0.338 Heterozygosity Within Populations
Ht 0.331 0.003 0.325 0.338 Total Heterozygosity
H't 0.331 0.003 0.325 0.338 Corrected total Heterozygosity
Gis 0.84 0.005 0.829 0.85 Inbreeding coefficient
Gst -0.001 0.003 -0.006 0.005 Fixation index
G'st(Nei) -0.001 0.003 -0.008 0.006 Nei, corrected fixation index
G'st(Hed) -0.001 0.005 -0.01 0.008 Hedrick, standardized fixation index
G''st -0.001 0.005 -0.011 0.008 Corrected standardized fixation index
D_est 0 0.002 -0.004 0.003 Jost, differentiation
*Standard deviations of statistics were obtained through jackknifing over loci.
**95% confidence intervals of statistics were obtained through bootstrapping over loci.
148
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BIOGRAPHICAL SKETCH
A biographical sketch is required of all candidates and it typically includes the
educational background of the candidate, but here it is partially replaced by the life story
of Judit Ungvari-Martin. She was born in Hungary, behind the iron curtain. She
remembers the first time she ate a banana in the summer, soon after the fall of
communism in the country. She was somewhat of a black sheep in the family for always
choosing outdoor activities, and anything involving animals or the family garden for
recreational activities. Despite her liking of all things biology and thinking about the
veterinary career, she attended law school 1998-1999 in Miskolc, Hungary. Very soon
she discovered that law is not her calling, and she ran off to volunteer on an Israeli
kibbutz where she worked in citrus groves, in the communal kitchen, in a plastic factory
and with toddlers in a preschool. She attended the Technion University and the
University of Haifa studying natural sciences and Hebrew. She moved to the United
States in 2003, got two BS degrees in 2007 in zoology and wildlife ecology and
conservation. She fell in love with working with birds while completing her
undergraduate Honors thesis with Gustavo Londoño. She fell in love with the tropics
after spending 6 month in Peru volunteering on a field project. Her dissertation research
is a product of 22 months in the field amongst amazing local people, volunteers from all
over the world, and all things that the Amazon can offer. Judit is committed to
enhancing the scientific capacity through direct interactions with students. She has
trained 44 undergraduate field assistants, advised 6 students through their honors
thesis projects. When not running around in the tropics, Judit enjoys life in places where
the weather is mild, cooking, baking, sharing life with family, friends, and pets. She is a
long time foster for pet rescue organizations.