View
231
Download
0
Embed Size (px)
Citation preview
7/30/2019 Boscolo&Metzger 2009 LandsEcol
1/12
R E S E A R C H A R T I C L E
Is bird incidence in Atlantic forest fragments influenced
by landscape patterns at multiple scales?
Danilo Boscolo Jean P. Metzger
Received: 6 June 2008 / Accepted: 2 June 2009 / Published online: 14 June 2009
Springer Science+Business Media B.V. 2009
Abstract The degree to which habitat fragmentation
affects bird incidence is species specific and may
depend on varying spatial scales. Selecting the correct
scale of measurement is essential to appropriately
assess the effects of habitat fragmentation on bird
occurrence. Our objective was to determine which
spatial scale of landscape measurement best describes
the incidence of three bird species (Pyriglena leucop-
tera, Xiphorhynchus fuscus and Chiroxiphia caudata)
in the fragmented Brazilian Atlantic forest and test if
multi-scalar models perform better than single-scalarones. Bird incidence was assessed in 80 forest
fragments. The surrounding landscape structure was
described with four indices measured at four spatial
scales (400-, 600-, 800- and 1,000-m buffers around
the sample points). The explanatory power of each
scale in predicting bird incidence was assessed using
logistic regression, bootstrapped with 1,000 repeti-
tions. The best results varied between species (1,000-
m radius for P. leucoptera; 800-m for X. fuscus and
600-m for C. caudata), probably due to their distinct
feeding habits and foraging strategies. Multi-scalemodels always resulted in better predictions than
single-scale models, suggesting that different aspects
of the landscape structure are related to different
ecological processes influencing bird incidence. In
particular, our results suggest that local extinction and
(re)colonisation processes might simultaneously act at
different scales. Thus, single-scale models may not be
good enough to properly describe complex pattern
process relationships. Selecting variables at multiple
ecologically relevant scales is a reasonable procedure
to optimise the accuracy of species incidence models.
Keywords Landscape structure
Spatial scale
Incidence
Fragmentation
AUC Atlantic plateau Pyriglena leucoptera
Xiphorhynchus fuscus Chiroxiphia caudata
Sao Paulo Brazil
Introduction
Birds living in fragmented habitats are frequently
subject to higher extinction risks than those incontinuous environments (Wiens 1995; Stratford
and Stouffer 1999; Brooker and Brooker 2001). This
occurs because fragmentation usually leads to
reduced habitat availability and may influence the
dispersal ability and spatial distribution of various
bird species (Clergeau and Burel 1997; Metzger
1998; Mazerolle and Villard 1999; Bakker et al.
2002). Some authors suggest that in landscapes with a
very low proportion of suitable habitat (less than 30%
D. Boscolo (&) J. P. Metzger
Department of Ecology, Institute of Bioscience,
University of Sao Paulo (USP), Rua do Matao, trav. 14, no
321, Cid. Universitaria, Sao Paulo 05508-900, Brazil
e-mail: [email protected]
123
Landscape Ecol (2009) 24:907918
DOI 10.1007/s10980-009-9370-8
7/30/2019 Boscolo&Metzger 2009 LandsEcol
2/12
of habitat cover), bird species survival may depend
mainly on the size and isolation of the remaining
patches (Andren 1994; Metzger and Decamps 1997).
Thus, reduced habitat cover, patch size and connec-
tivity have been argued to have negative effects on
tropical forest birds (Sekercioglu et al. 2002; Cas-
telletta et al. 2005; Develey and Metzger 2006). Thesensitivity to each of these factors may vary among
species (Ferraz et al. 2007). Uezu et al. (2005) found
that frugivorous birds in the fragmented Brazilian
Atlantic forest were more affected by patch size than
insectivorous species, which were more abundant in
patches connected to other forests by corridors.
Similarly, Martensen et al. (2008) found that Atlantic
forest birds of different functional groups, such as
terrestrial or understory insectivores, were differently
affected by patch area and connectivity.
These studies, however, did not take into accountthe spatial scale at which landscape parameters were
measured. In fragmented habitats, the degree to
which the landscape structure influences the inci-
dence of a species can depend on processes
happening at varying spatial scales (Gutzwiller and
Anderson 1987; Wiens 1989; Levin 1992; Linden-
mayer 2000; Cushman and McGarigal 2004; Verg-
ara and Armesto 2009), considering either the
landscape extent (Fuhlendorf et al. 2002) or grain
(Rahbek and Graves 2001; Meyer and Thuiller
2006). Bird occurrence and abundance may actuallybe related to the spatial range in which individuals
can perceive or be affected by different aspects of
the surrounding environment that happen at different
scales, such as habitat heterogeneity and isolation
(van Rensburg et al. 2002; Ewers and Didham
2006).
Lawler and Edwards (2002) suggest that selecting
the right scale to assess the effects of landscape
structure on bird incidence is essential for deriving
useful predictive habitat models. Some authors even
indicate that using multi-scalar approaches to pro-duce these models for different species (mammals
and birds) can yield better models than single-scalar
approaches (Jaquet 1996; Lindenmayer 2000; Graf
et al. 2005). Considering each factor at its most
appropriate scale may help to better describe the
species relationship to the surrounding environment.
However, studies of model ecological systems com-
paring the effects of using single and multi-scalar
approaches are rare, even though some authors have
stressed the need for them (Martnez et al. 2003; Wu
2007; Renfrew and Ribic 2008).
According to Li and Wu (2007), the effects of
spatial patterns on ecological processes can be
misleading because choosing the wrong scale of
measurement can hide important aspects of landscape
structure and composition that modify the observedsystem at coarser or more refined levels. This issue
should be taken into account when habitat models
relating bird incidence to landscape structure data are
constructed (Thompson and McGarigal 2002; Graf
et al. 2005). In such cases, the selection of the correct
spatial scale to measure landscape structure and the
choice between a single or multi-scalar approach are
essential decisions when assessing how habitat frag-
mentation can affect the incidence and persistence of
different bird species.
Our objectives in this study were: (1) to determinewhich spatial scale of measurement best describes the
incidence patterns of three small passerine bird
species found in the fragmented Atlantic forest in
southeastern Brazil and (2) to compare the perfor-
mance of single and multi-scalar approaches in
predicting bird occurrence. Due to severe deforesta-
tion, the Brazilian Atlantic forest is currently com-
posed of extremely small and isolated remnants
(Ribeiro et al. 2009), and the processes affecting
species survival in such an environment are expected
to be caused mainly by changes in landscapestructure (Goodwin and Fahrig 2002). Within the
last few years, some studies have tried to relate
understory bird distribution patterns to the structure
of fragmented Atlantic forest landscapes (Uezu et al.
2005; Develey and Metzger 2006), but they did not
account for the effects of inappropriate scale choice
on the accuracy of their results. To properly under-
stand processes effects on forest birds persistence
and incidence in the Atlantic forest, we need to assess
the accuracy and explanatory power of landscape
structure measurements at varying scales.
Methods
Study sites
For this study we selected 80 Atlantic forest
fragments in the southwest portion of Sao Paulo
state, on the Atlantic Plateau of Sao Paulo, Brazil
908 Landscape Ecol (2009) 24:907918
123
7/30/2019 Boscolo&Metzger 2009 LandsEcol
3/12
(Boscolo 2007). The relief is largely characterised by
convex hills with a low density of deep valleys (Ross
and Moroz 1997). The climate is predominantly
temperate, warm and rainy. The original forest cover
in the region was classified as dense montane
ombrophylous forests (Oliveira-Filho and Fontes
2000), but the use of natural wooded areas foragricultural fields, logging and charcoal production
has severely fragmented it. In the present day, most
of the natural vegetation fragments found on the
Atlantic Plateau are of second-growth forests of
varying ages and sizes. These forests are composed of
about 220 tree species, most of them from the
Fabaceae, Myrtaceae and Rubiacea families. Despite
their richness, these secondary forests differ signif-
icantly in species composition from more mature
forests found in an adjacent forest reserve (Bernacci
et al. 2006; Durigan et al. 2008).We chose fragments embedded in a wide range of
forest cover (570% within an 800-m buffer) and
connectivity conditions (proximity index ranges from
1.5 to 250.0 with an 800-m search radius; McGarigal
and Marks 1995). The minimum distance from a
focal fragment to the nearest forest was 20 m and the
maximum 260 m. Fragment size ranged from 1.2 to
274.3 ha, with a mean area of 34.3 ha. To reduce
variation related to matrix composition and habitat
quality, we intentionally selected only second-growth
fragments with similar internal forest structures thatwere surrounded mainly by non-forested field matri-
ces (Boscolo 2007).
Selected species
We selected for the present study three passerine bird
species that are strictly associated with forest and are
unable to survive in non-forested environments. All
species are nonmigratory year-round residents, exhi-
bit strong territorial behaviour and are known to
respond to playback stimuli (Stotz et al. 1996).Playback methods to determine their presence/
absence pattern have been studied and are consoli-
dated, making their survey more precise and efficient
(Boscolo et al. 2006). Because of their different
home-range sizes and abilities to move through the
non-forested matrix, they are expected to perceive
and react to the structure of the surrounding land-
scape with distinct sensitivities and at different scales
(Sick1997; Goerck1999; Melo-Junior et al. 2001). In
addition, all three species are typical of three
different widespread families of the Atlantic forest
with very distinct biological traits and can be used to
evaluate the effect of landscape scale on species with
different ecological profiles.
Chiroxiphia caudata (Pipridae) is a small omniv-
orous bird that lives in groups with a stronghierarchical structure (Foster 1981; Sick 1997), a
common characteristic of its family. It is able to cross
up to 130 m of open matrix (Uezu et al. 2005) and
has an average home-range size of 8 ha (Hansbauer
et al. 2008). Xiphorhynchus fuscus (Dendrocolapti-
dae) is commonly seen in mixed bird flocks and, like
most of the species in its family, can only land on
upright logs (Brooke 1983; Soares and dos Anjos
1999). Individuals crossing an open matrix between
forest patches must, therefore, do it in a single flight,
which might limit the birds dispersal ability. Thespecies expected habitat gap crossing ability is
150 m, and its home range is around 6 ha (Develey
1997; Boscolo et al. 2008). Pyriglena leucoptera
(Thamnophilidae) is an ant-following bird that
inhabits the understory of dense forests. Having a
home-range size of about 15 ha (Hansbauer et al.
2008) and a gap crossing ability of only 60 m (Uezu
et al. 2005), it is the most sensitive of the three
species to habitat loss and fragmentation (dos Anjos
and Bocon 1999).
Bird surveys
We collected bird species presence/absence data with
the use of playback census techniques at one point
per fragment located inside the forest and near the
centre of each fragment. All surveys were done by the
same person (DB) to avoid observer bias. The
employed survey method was adapted from Boscolo
et al. (2006) and consisted of broadcasting the songs
of male birds to actively stimulate them and increase
detection rates by making quiet individuals notice-able. Surveys occurred at the times of the day with
the highest bird detection rates when using playbacks
(Boscolo et al. 2006), namely sunrise and in the 2 h
around noon. Boscolo et al. (2006) also attest that
with the use of playback stimuli, the detectability of
these birds does not vary throughout the year.
For all species, each playback session lasted
5 min, followed by five more minutes of silent
observation, which was enough to account for late
Landscape Ecol (2009) 24:907918 909
123
7/30/2019 Boscolo&Metzger 2009 LandsEcol
4/12
responsive birds. We noted a species as present at a
given point if at least one individual was heard or
seen within the surveyed fragment during or after the
playback. We repeated the surveys in all fragments
for 3 days in different weeks within 2 months of the
first survey of each sample point. If after this time nobird was detected at a certain point, we assumed the
species to be absent at this location. According to
Boscolo et al. (2006), three 10-min surveys on non-
consecutive days at a given location can assure for
these species a probability greater than 95% of
correct absence detection. In this manner, it was
possible to assess bird occurrence with a reduced risk
of false absence records (Thompson 2002), result-
ing in very precise presence/absence data. We
conducted the bird surveys within the dry seasons
from April 2004 to November 2005. Due to noiseinterference with bird detection, we did not execute
playback sessions during rainy days or days with
winds stronger than three in the Beaufort scale.
Landscape structure
We generated maps of forest cover for the studied
region using ground-truth field observations con-
ducted together with the selection of study sites and
subsequent supervised classification of Landsat TM5
satellite images (bands 3, 4 and 5) from 2001.
Because all fragments in the studied region consisted
of similar second-growth forests and the landscape
matrix mainly of open field habitats, we classified
land cover into only two classes, forest and non-forest. The final maps consisted of raster files with
30-m pixel sizes for all landscapes. Based on ground-
truthing, all maps accuracies were[90%.
We plotted all bird sampling points on the digital
maps and used them as central references to define
concentric circular buffers of varying radii represent-
ing distinct spatial extents or scales (Wu 2007). We
used these buffers to subset the original classified
images, generating round landscape maps of varying
sizes (Fig. 1). We analysed the forest spatial structure
inside each round landscape based on four landscapeindices (Table 1) using FRAGSTATSTM (McGarigal
and Marks 1995). All of these indices described
either the connectivity or amount of available habitat
in the landscape, factors we expected to directly
affect bird occurrence patterns (Taylor et al. 1993;
Wiens 1995; Fahrig 2003; Develey and Metzger
2006).
We selected four spatial scales to be compared:
400-, 600-, 800- and 1,000-m radius. The total
Fig. 1 Example of round
landscape maps subset from
the original classified
images. a Part of the
original map with
concentric circles (grey
lines) around a sampling
point; b subset of roundlandscapes of varying radii,
with bird sampling point in
the centre. Black polygon:
sampled forest fragment;
Light grey polygons: other
forests; White cross
sampling point location
910 Landscape Ecol (2009) 24:907918
123
7/30/2019 Boscolo&Metzger 2009 LandsEcol
5/12
landscape areas of each scale were correspondingly:
50.26, 113.10, 201.06 and 314.16 ha. These extentswere chosen as tentative scales and are considered
reasonable for most understory birds with home-
range sizes up to 15 ha (Develey and Metzger 2006).
We did not use smaller scales because they were too
restrictive, and not all indices could be correctly
measured. We also set an upper scale limit of
1,000 meters to avoid the problem of strong spatial
autocorrelation of the round local landscapes.
Scale comparison
To evaluate the spatial scales at which the landscape
structure best explained the birds occurrence pat-
terns, we modelled their incidence using logistic
regression with landscape indices as explanatory
variables. We built single and multi-scale models,
both including two landscape indices, using binomial
(logit link) generalised linear models (GLM). Single-
scale models were those in which both explanatory
variables belonged to the same spatial scale, while
multi-scale refers to the models containing indepen-
dent variables of distinct scales in its structure. Weassessed each models explained variance using its
adjusted R2 value and estimated its significance
through log-likelihood (v2) tests. To avoid including
two significant highly correlated variables in the same
model, we pairwise selected variables using the
Spearman correlation rank rs (Green 1979; Fielding
and Haworth 1995). For each species, all models had
the same set of two independent variables regardless
of scale. Even though ecological requirements of a
species can be described by a different set of factors,
this was done to standardise the models structure in
order to maintain comparability among scales. For all
analyses, we set alpha at 0.05. To avoid strong spatial
autocorrelation among variables, no model included
the same index more than once, even at different
scales.We assessed the accuracy of all models using the
Receiver Operating Characteristic curve (ROC, Del-
eo 1993). From this analysis, it was possible to
calculate the area under the ROC function curve
(AUC). The AUC is a widely used threshold-
independent measure of overall model accuracy and
can be used to compare model strength (Brotons et al.
2004; Graf et al. 2005). For instance, an AUC value
of 0.8 indicates that 80% of the time, a random data
point with observed bird presence will have an
occurrence probability higher than a random point inwhich birds were absent.
With the aim of determining for each species which
of the models among all single and multi-scale models
could on average perform best, we used the bootstrap
procedure (Efron 1979) to calculate the mean model
accuracy, explained variance and log-likelihood for
all possible scale combinations among the two
variables included. The bootstrap procedure consisted
of randomly selecting for the models only 60 of the 80
existing data points, repeating this selection 1,000
times with repositions. We were thus able to generatelarge distributions of AUC, R2 and log-likelihood
values for each scale combination. We selected which
variable combination would be analysed for each
species based on the highest mean R2 values derived
from the bootstrap procedure. The resulting AUC, R2
and log-likelihood distributions of the selected single-
scale and the best explanatory multi-scale models
were compared using single-factor analyses of vari-
ance (ANOVA). Between-groups effects were
assessed a posteriori through the Tukey post hoc test.
All statistical analyses were conducted with the Rstatistical package (R Development Core Team 2005)
using the Hmisc (version 3.0-1) and Design
(version 2.0-9) libraries (Harrell 2001).
Results
All variables were positively correlated with each
other, except for the mean euclidian distance to the
Table 1 Indices used to describe the landscape structure
around each sample point at three different spatial scales
Variable
code
Variable
name
Description
PFOREST Proportion of
forest
Proportion of the landscape
covered by forest
PD Patch density Number of fragments in the
round landscapes divided
by total landscape area
AREAMN Mean patch area Mean area of all forest
patches in the landscape
ENNMN Mean Euclidean
nearest-
neighbour
distance
Mean Euclidian distance to
the nearest neighbour
patch averaged for all
patches in the landscape
Landscape Ecol (2009) 24:907918 911
123
7/30/2019 Boscolo&Metzger 2009 LandsEcol
6/12
nearest patch (ENNMN), which was negatively
correlated with every other variable regardless of
the scale considered (Table 2). Most of the variables
were significantly correlated. Only the correlations of
the mean patch area (AREAMN) with ENNMN and
of the proportion of forest (PFOREST) and patch
density (PD) were in general small (Table 2). Con-sequently, the models simultaneously contained
either AREAMN and ENNMN or PFOREST and
PD. According to the results of the bootstrap
procedure, the variable combinations with the highest
R2 for P. leucoptera and X. fuscus were PFOREST
and PD. For C. caudata, the selected models included
AREAMN and ENNMN.
Among all four indices, the birds incidence
patterns were negatively related only to ENNMN.
Almost all models were on average significant
(Table 3). In the case of the single-scale models, meanAUC increased with local landscape size for P.
leucoptera and X. fuscus, reaching its highest values
for local landscapes defined with 1,000- and 800-m
radii around the sample points, respectively (Fig. 2).
The best single-scale model to predict the incidence of
C. caudata was at the 600-m scale (Fig. 2). It is
interesting to notice that all mean AUC, R2 and log-
likelihood values had consistently low standard devi-
ations (Table 3), indicating low variation and good
reliability of models generated from randomly
selected data points.The analysis of variance indicated that both AUC
and R2
values presented significant differences
between scales within each species. According to
the Tukey test, the accuracy (AUC) of all scales was
significantly different for both P. leucoptera and C.
caudata. Nevertheless, the 600- and 1,000-m scales of
X. fuscus had equal accuracies (Fig. 2) and explained
variances (R2
). The multi-scalar approach always
resulted in significantly higher model accuracy and
explanatory power for all species (P\ 0.01), even
when the differences were apparently small. Thisindicates better general performance of such models
compared to the single-scalar models.
Discussion
Our results show that variations of the scale at which
the landscape structure of fragmented Atlantic forest
is measured seem to be a key factor for the power ofTable2
SpearmanrcorrelationindexandP
valuesbetweenallvariable
satthefourspatialscales(N=
80for
eachvariableandscale)
PD(400)
PD(600)
PD(800)
PD(1,0
00)
AREAMN(400)
A
REAMN(600)
AREAMN(800)
AREAMN(1,
000)
ENNMN(400)
ENNMN(600)
ENNMN(800)
ENNMN(1,0
00)
PFOREST(400)
-.1
923ns
-.1
268n
s
.0222ns
-.1
22ns
.6855***
.5708***
.6196***
.4420
-.3
858***
-.4
081***
-.45
32***
-.2
993***
PFOREST(600)
-.0
549ns
-.0
124n
s
.1319ns
.0116ns
.5929***
.6119***
.6063***
.5244***
-.3
456**
-.4
514***
-.54
73***
-.4
199***
PFOREST(800)
.0619ns
.1231n
s
.2439*
.1543ns
.4766***
.5247***
.5364***
.5252***
-.3
271**
-.4
595***
-.58
06***
-.4
981***
PFOREST(1,
000)
.1413ns
.2113n
s
.3498**
.2773*
.3831***
.4632***
.4506***
.4974***
-.3
211**
-.4
622***
-.59
09***
-.5
319***
PD(400)
-.5
654***
-
.3160*
-.3
579**
-.1
702ns
-.2
942**
-.3
019**
-.34
00**
-.3
361***
PD(600)
-.4
350***
-
.4125****
-.4
188***
-.2
657*
-.2
732**
-.4
064***
-.44
57***
-.5
041***
PD(800)
-.2
697*
-
.2037ns
-.4
497***
-.2
242*
-.3
305***
-.4
117***
-.52
43***
-.5
516***
PD(1,
000)
-.3
964***
-
.3062**
-.4
267***
-.2
426*
-.2
564*
-.3
709**
-.45
44***
-.6
242***
AREAMN(400)
.0136ns
-.0
674ns
-.14
06ns
-.0
219ns
AREAMN(600)
-.0
213ns
-.0
428ns
-.16
95ns
-.0
561ns
AREAMN(800)
-.0
561ns
-.1
477ns
-.11
90ns
-.0
133ns
AREAMN(1,
000)
-.1
256ns
-.1
675ns
-.19
07ns
-.0
570ns
Thenumbersinparenthesesindicatethescale(radius,inmeters)ofeachvariable.
SeeTable1forvariablenamesandcodes
nsNonsignificant
*P\
0.0
5;**P\
0.0
1;***P\
0.0
01
912 Landscape Ecol (2009) 24:907918
123
7/30/2019 Boscolo&Metzger 2009 LandsEcol
7/12
incidence models to predict the presence/absence of
bird species. The low R2 values (\0.5) presented by
the models indicate that other factors not measured
here that also influence bird occurrence might exist.
This study, however, did not intend to evaluate the
effects of the whole set of environmental aspects that
may affect bird incidence, but only of those related to
landscape structure at varying scales. The way
landscape structure variables measured at different
spatial scales influenced the model results was unique
for each species. This specificity is directly related to
the extent to which each of them perceives its
environment and arises from its biological character-
istics (Levin 1992; Meyer and Thuiller 2006).
Furthermore, for all species, multi-scale models
performed better than the single-scale ones.
Considering only the single-scale models, the best
spatial scale to predict the incidence ofP. leucoptera
Table 3 Results of the bootstrap procedure with 1,000 replications for each species multiple logistic regressions at all four scales
and best explanatory multi-scale model (used scale in parentheses)
Species Scale Multivariate models b AUC R2 v2
P. leucoptera 400 PFOREST 0.0491 0.008 0.782 0.03 0.296 0.06 15.10 3.5***
PD 0.0700 0.031
600 PFOREST 0.0767 0.012 0.815 0.03 0.373 0.06 19.73 3.9***PD 0.4131 0.250
800 PFOREST 0.0867 0.016 0.821 0.03 0.388 0.07 20.73 4.3***
PD 0.1206 0.051
1,000 PFOREST 0.0944 0.018 0.825 0.03 0.384 0.06 20.47 4.3***
PD 0.6854 0.410
Multi-scale PFOREST (600) 0.0776 0.012 0.831 0.03 0.432 0.06 23.52 4.1***
PD (1,000) 1.8464 0.449
X. fuscus 400 PFOREST 0.0414 0.013 0.747 0.04 0.215 0.07 9.45 3.3**
PD 0.1023 0.030
600 PFOREST 0.0513 0.017 0.800 0.03 0.307 0.06 13.99 3.3***
PD 0.2046 0.051
800 PFOREST 0.0591 0.02 0.836 0.03 0.383 0.06 18.00 3.6***
PD 0.3617 0.080
1,000 PFOREST 0.0886 0.029 0.800 0.03 0.314 0.06 14.36 3.5***
PD 0.9652 0.582
Multi-scale PFOREST (400) 0.0369 0.012 0.841 0.03 0.402 0.06 19.02 3.8***
PD (800) 0.4710 0.082
C. caudata 400 AREAMN 0.0875 0.117 0.675 0.06 0.136 0.08 4.44 2.60
ENNMN -0.0123 0.006
600 AREAMN 0.1030 0.152 0.818 0.04 0.418 0.10 17.06 4.7***
ENNMN -0.0295 0.007
800 AREAMN 0.1695 0.129 0.758 0.05 0.185 0.08 7.20 3.5*
ENNMN -0.0120 0.004
1,000 AREAMN 0.2425 0.142 0.784 0.04 0.186 0.09 7.26 3.7*
ENNMN -0.0104 0.005
Multi-scale AREAMN (400) 0.1621 0.107 0.854 0.05 0.469 0.09 19.51 4.5***
ENNMN (600) -0.0322 0.008
N= 60 for each variable pair and repetition. b mean regression coefficient; AUC, mean model accuracy; R2, mean model variance
explained; v2, mean log likelihood test (df= 2 for all regressions). All mean values are presented with standard deviations. See
Table 1 for variable names and codes
* P\ 0.05; ** P\0.01; *** P\ 0.001
Landscape Ecol (2009) 24:907918 913
123
7/30/2019 Boscolo&Metzger 2009 LandsEcol
8/12
was 1,000 m, for X. fuscus 800 m and for C. caudata
one scale lower (600 m). These inter-specific varia-
tions may be primarily linked to the range of activity
of each species. It is expected that the occurrence
patterns of birds that have larger territories should be
affected by larger spatial scales than those of birds
with smaller area needs (Wiens 1989; Lawler and
Edwards 2002; Thompson and McGarigal 2002; Graf
et al. 2005). In fact, other studies within the same
region have shown that the mean home-range size of
P. leucoptera in fragmented landscapes is approxi-
mately 15 ha, about double the size observed for C.
caudata (8 ha; Hansbauer et al. 2008). However, the
home-range of X. fuscus (for which the better scalewas larger than for C. caudata) is approximately 6 ha
(Develey 1997), suggesting that factors other than
habitat requirements may be influencing the birds
sensitivity to landscape structure at different scales.
Functionally, the differences in the best scale may
be also related to the birds feeding characteristics.
The best scales for both insectivorous species were
larger than for C. caudata, which is omnivorous,
(Sick 1997; del Hoyo et al. 2003a, b). According to
some studies in tropical forests (Davis 1945; Roberts
et al. 2000; Develey and Peres 2000), the availabilityof arthropod resources in the forest may vary
considerably in time and space, reducing the feeding
resources available to strictly insectivorous birds
depending on the season and landscape structure.
This would force them to periodically increase their
range of activity in search of available food. On the
other hand, C. caudata may be less sensitive to
landscape structure variations at large scales because
it can probably avoid local resource scarcity by
shifting between insects and fruits (Snow 1976),
reducing the need to wander far in search ofresources. However, the effects of landscape structure
variation for insectivorous versus omnivorous tropi-
cal birds have yet to be tested.
Another hypothesis to explain the better perfor-
mance of larger scales for the two insectivorous birds
relates to their foraging strategies. Pyriglena leucop-
tera is constantly found following ant swarms to feed
on fleeing small animals (Willis and Oniki 1978; Sick
1997; Gomes et al. 2001; del Hoyo et al. 2003a).
Because these are moving resources dispersing over
large areas and different habitat types (Roberts et al.2000), P. leucoptera is probably compelled to follow
them, becoming subject to resource availability at
larger scales compared to the other species. On the
other hand, X. fuscus is common in mixed bird flocks
(Goerck 1999; Maldonado-Coelho and Marini 2000;
Develey and Peres 2000). Because these bird groups
may occupy areas much larger than the mean home-
range of X. fuscus, the influence of landscape
structure on its incidence would take place at bigger
Fig. 2 Mean model accuracy (AUC) of the single-scale and
best multi-scale models, with standard error bars, for each of
the species. The dashed lines indicate the highest mean
accuracy among each species models. The spatial scale is
represented as the radius from the sample points used to define
each round local landscape. N= 1,000 for each scale and
species. For each species, different letters above mean plots
indicate significant differences as verified by the Tukey posthoc test
914 Landscape Ecol (2009) 24:907918
123
7/30/2019 Boscolo&Metzger 2009 LandsEcol
9/12
scales. This process may also explain why the
incidence of X. fuscus was better predicted by a
larger scale compared to C. caudata, even though this
last species presents larger home-ranges.
In addition to these characteristics, because the
forest spatial structure was measured using areas
considerably larger than the mean home ranges of thebirds, the occurrence patterns found in the present
study may also be related to the birds aptitude at
moving among habitat remnants and maintaining
viable populations in fragmented landscapes. At this
level, the persistence of a species depends on local
extinction rates and patch accessibility (Hanski 1994;
Lindenmayer et al. 1999; Brooker and Brooker 2001;
Bakker et al. 2002; Sekercioglu et al. 2002). While
these two factors directly influence birds incidence
patterns, they also arise from distinct ecological
processes that might simultaneously happen at dif-ferent spatial scales. Local extinctions may be
influenced by resource availability, which depends
on foraging strategies and small scale internal habitat
characteristics (Major et al. 1999; Stratford and
Stouffer 1999; Beier et al. 2002). At the same time,
patch accessibility is altered by forest connectivity
and depends on the species moving abilities and the
spatial arrangement of several habitat patches in a
larger landscape scale (Taylor et al. 1993; Wiens
1995; Brooker and Brooker 2001; Heinz et al. 2005).
The influence on species survival of severalecological processes happening at different scales is
probably the reason why the multi-scale models were
more accurate and presented higher explained vari-
ance than the best single-scale ones. In the case of the
species we studied, variables that are strongly related
to the amount of surrounding available habitat,
namely PFOREST and AREAMN (Neel et al.
2004), may directly influence birds chances of
finding good feeding and breeding sites at the scale
of individual territories. At the same time, isolation
(ENNMN) and fragmentation (PD) measures may bemore related to general landscape restrictions of bird
movements between patches at a larger scale, prob-
ably influencing individual dispersal and patch
recolonisation. Evidence of multi-scalar responses
to landscape structure has also been found for other
tropical species, such as Australian parrots (Manning
et al. 2006) and opossums (Lindenmayer 2000).
Equally, Thompson and McGarigal (2002) found that
the American eagle (Haliaeetus leucocephalus)
chooses its habitat depending on resource selection
or environmental disturbance at multiple scales.
In the present study, the considerably better
performance of the multi-scalar models indicates that
single-scale models may not be good enough to
properly describe the complex interactions between
species ecology and landscape patterns. Because therelationships between bird ecology, population pro-
cesses and landscape structure might function in a
multi-scalar way (Wu 2007), the use of different
variables in multiple ecologically relevant scales is a
reasonable procedure to optimise the accuracy and
explanatory power of bird incidence models. Studies
that aim to assess the multiple effects of landscape
structure on small tropical passerine birds found in
fragmented forests should carefully consider each
spatial scale of each variable as potentially relevant
and test the use of more than a single scale wheneverpossible.
Acknowledgments We would like to thank the Helmholtz
Institut fur UmweltforschungUFZ for institutional support,
Roland Graf, Carlos Rodrguez, Milton Cezar Ribeiro, Paulo
de Marco Junior and the staff from LEPaC for their assistance
in the data analysis and comments on previous versions of this
manuscript, and Milton Cezar Ribeiro for aiding us with the
image classifications, GIS and Bootstrap procedures. This
research was supported by CNPq Conselho Nacional de
Desenvolvimento Cientfico e Tecnologico, an institution of
the Brazilian government dedicated to the development of
science.
References
Andren H (1994) Effects of habitat fragmentation on birds and
mammals in landscapes with different proportions of
suitable habitat: a review. Oikos 71:355366. doi:10.2307/
3545823
Bakker KK, Naugle DE, Higgins KF (2002) Incorporating
landscape attributes into models for migratory grassland
bird conservation. Conserv Biol 16:16381646. doi:
10.1046/j.1523-1739.2002.01328.x
Beier P, van Drielen M, Kankam BO (2002) Avifaunal collapse
in West African forest fragments. Conserv Biol 16:1097
1111. doi:10.1046/j.1523-1739.2002.01003.x
Bernacci LC, Franco GADC, Arbocz GF, Catharino ELM,
Durigan G, Metzger JP (2006) O efeito da fragmentacao
florestal na composicao e riqueza de arvores na regiao da
reserva Morro Grande (planalto de Ibiuna, SP). Rev Inst
Flor 18:121166
Boscolo D (2007) Influencia da estrutura da paisagem sobre a
persistencia de tres especies de aves em paisagens frag-
mentadas da Mata Atlantica. Dissertation, University of
Landscape Ecol (2009) 24:907918 915
123
http://dx.doi.org/10.2307/3545823http://dx.doi.org/10.2307/3545823http://dx.doi.org/10.1046/j.1523-1739.2002.01328.xhttp://dx.doi.org/10.1046/j.1523-1739.2002.01003.xhttp://dx.doi.org/10.1046/j.1523-1739.2002.01003.xhttp://dx.doi.org/10.1046/j.1523-1739.2002.01328.xhttp://dx.doi.org/10.2307/3545823http://dx.doi.org/10.2307/35458237/30/2019 Boscolo&Metzger 2009 LandsEcol
10/12
Sao Paulo, Brazil. Available in English from http://
www.teses.usp.br/teses/disponiveis/41/41134/tde-130220
08-180423/. Accessed 10 June 2009
Boscolo D, Metzger JP, Vielliard JME (2006) Efficiency of
playback for assessing the occurrence of five bird species in
Brazilian Atlantic Forest fragments. An Acad Bras Cienc
78:629644. doi:10.1590/S0001-37652006000400003
Boscolo D, Candia-Gallardo C, Awade M et al (2008)
Importance of inter-habitat gaps and Stepping-stones for
lesser woodcreepers (Xiphorhynchus fuscus) in the
Atlantic Forest, Brazil. Biotropica 40:273276. doi:
10.1111/j.1744-7429.2008.00409.x
Brooke MD (1983) Ecological segregation of woodcreepers
(Dendrocolaptidae) in the state of Rio-De-Janeiro,
Brasil. Ibis 125:562567. doi:10.1111/j.1474-919X.1983.
tb03150.x
Brooker M, Brooker L (2001) Breeding biology, reproductive
success and survival of blue-breasted fairy-wrens in
fragmented habitat in the western Australian wheatbelt.
Wildl Res 28:205214. doi:10.1071/WR00012
Brotons L, Thuiller W, Araujo MB et al (2004) Presence-
absence versus presence-only modelling methods forpredicting bird habitat suitability. Ecography 27:437448.
doi:10.1111/j.0906-7590.2004.03764.x
Castelletta M, Thiollay JM, Sodhi NS (2005) The effects of
extreme forest fragmentation on the bird community of
Singapore Island. Biol Conserv 121:135155. doi:
10.1016/j.biocon.2004.03.033
Clergeau P, Burel F (1997) The role of spatio-temporal patch
connectivity at the ladscape level: an example in a bird
distribution. Landsc Urban Plan 38:3743. doi:10.1016/
S0169-2046(97)00017-0
Cushman SA, McGarigal K (2004) Patterns in the species-
environment relationship depend on both scale and choice
of response variables. Oikos 105:117124. doi:10.1111/
j.0030-1299.2004.12524.xDavis DE (1945) The annual cycle of plants, mosquitoes, birds,
and mammals in 2 Brazilian forests. Ecol Monogr
15:243295. doi:10.2307/1943247
del Hoyo J, Elliott A, Christie DA (eds) (2003a) Handbook of
the birds of the world. vol. 8. Broadbills to Tapaculos.
Lynx Edicions, Barcelona
del Hoyo J, Elliott A, Christie DA (eds) (2003b) Handbook of
the birds of the world. Vol 9. Cotingas to Pipits and
Wagtails. Lynx Edicions, Barcelona
Deleo JM (1993) Receiver operating characteristic laboratory
(ROCLAB): software for developing decision strategies
that account for uncertainty. In: IEE (ed) Proceedings of
the second international symposium on uncertainty mod-
eling and analysis. Computer Society Press, College Park
Develey PF (1997). Ecologia de bandos mistos de aves de Mata
Atlantica na estacao Ecologica Jureia Itatins. Dissertation,
University of Sao Paulo
Develey PF, Peres CA (2000) Resource seasonality and the
structure of mixed species bird flocks in a coastal Atlantic
forest of southeastern Brazil. J Trop Ecol 16:3353. doi:
10.1017/S0266467400001255
Develey PF, Metzger JP (2006) Emerging threats to birds in
Brazilian Atlantic forests: the roles of forest loss and
configuration in a severely fragmented ecosystem. In:
Laurance WF, Peres CA (eds) Emerging threats to tropical
forests. University of Chicago Press, Chicago, pp 269290
dos Anjos L, Bocon R (1999) Bird communities in natural forest
patches in southern Brazil. Wilson Bull 111:397414
Durigan G, Bernacci LC, Franco GADC, Arbocz GF, Metzger
JP, Catharina ELM (2008) Estadio sucessional e fatores
geograficos como determinantes da similaridade florstica
entre comunidades florestais no Planalto Atlantico, Estado
de Sao Paulo, Brasil. Acta bot bras 22:5162
Efron B (1979) Bootstrap methods: another look at the jack-
knife. Stat 7:126
Ewers RM, Didham R (2006) Confounding factors in the
detection of species responses to habitat fragmentation.
Biol Rev Camb Philos Soc 81:117142. doi:10.1017/
S1464793105006949
Fahrig L (2003) Effects of habitat fragmentation on biodiver-
sity. Annu Rev Ecol Evol Syst 34:487515. doi:
10.1146/annurev.ecolsys.34.011802.132419
Ferraz G, Nichols JD, Hines JE, Stouffer PC, Bierregard RO Jr,
Lovejoy TE (2007) A large-scale deforestation experi-
ment: effects of patch area and isolation on Amazon birds.
Science 315:238241. doi:10.1126/science.1133097Fielding AH, Haworth PF (1995) Testing the generality of
bird-habitat models. Conserv Biol 9:14661481. doi:
10.1046/j.1523-1739.1995.09061466.x
Foster MS (1981) Cooperative behavior and social organiza-
tion of the Swallow-tailed Manakin (Chiroxiphia cauda-
ta). Behav Ecol Sociobiol 9:167177. doi:10.1007/
BF00302934
Fuhlendorf SD, Woodward AJW, Leslie DM et al (2002)
Multi-scale effects of habitat loss and fragmentation on
lesser prairie-chicken populations of the US Southern
Great Plains. Landscape Ecol 17:617628. doi:10.1023/
A:1021592817039
Goerck JM (1999) Distribution of birds along an elevational
gradient in the Atlantic forest of Brazil: implications forthe conservation of endemic and endangered species. Bird
Life Int 9:235253
Gomes VSM, Alves VS, Ribeiro JRI (2001) Intens alimentares
encontrados em amostras de regurgitacao de Pyriglena
leucoptera (Vieillot) (Aves, Thamnophilidae) em uma
floresta secundaria no Estado do Rio de Janeiro. Rev Bras
Zool 18:10731079
Goodwin BJ, Fahrig L (2002) How does landscape structure
influence landscape connectivity? Oikos 99:552570. doi:
10.1034/j.1600-0706.2002.11824.x
Graf RF, Bollmann K, Suter W et al (2005) The importance of
spatial scale in habitat models: capercaillie in the Swiss
Alps. Landscape Ecol 20:703717. doi:10.1007/s10980-
005-0063-7
Green R (1979) Sampling design and statistical methods for
environmental biologists. Wiley, New York
Gutzwiller KJ, Anderson SH (1987) Multiscale associations
between cavity-nesting birds and features of Wyoming
streamside woodlands. Condor 89:534548. doi:10.2307/
1368643
Hansbauer MM, Storch I, Pimentel RG, Metzger JP (2008)
Comparative range use by three Atlantic Forest understory
bird species in relation to forest fragmentation. Trop Ecol
J 24:291299
916 Landscape Ecol (2009) 24:907918
123
http://www.teses.usp.br/teses/disponiveis/41/41134/tde-13022008-180423/http://www.teses.usp.br/teses/disponiveis/41/41134/tde-13022008-180423/http://www.teses.usp.br/teses/disponiveis/41/41134/tde-13022008-180423/http://dx.doi.org/10.1590/S0001-37652006000400003http://dx.doi.org/10.1111/j.1744-7429.2008.00409.xhttp://dx.doi.org/10.1111/j.1474-919X.1983.tb03150.xhttp://dx.doi.org/10.1111/j.1474-919X.1983.tb03150.xhttp://dx.doi.org/10.1071/WR00012http://dx.doi.org/10.1111/j.0906-7590.2004.03764.xhttp://dx.doi.org/10.1016/j.biocon.2004.03.033http://dx.doi.org/10.1016/S0169-2046(97)00017-0http://dx.doi.org/10.1016/S0169-2046(97)00017-0http://dx.doi.org/10.1111/j.0030-1299.2004.12524.xhttp://dx.doi.org/10.1111/j.0030-1299.2004.12524.xhttp://dx.doi.org/10.2307/1943247http://dx.doi.org/10.1017/S0266467400001255http://dx.doi.org/10.1017/S1464793105006949http://dx.doi.org/10.1017/S1464793105006949http://dx.doi.org/10.1146/annurev.ecolsys.34.011802.132419http://dx.doi.org/10.1126/science.1133097http://dx.doi.org/10.1046/j.1523-1739.1995.09061466.xhttp://dx.doi.org/10.1007/BF00302934http://dx.doi.org/10.1007/BF00302934http://dx.doi.org/10.1023/A:1021592817039http://dx.doi.org/10.1023/A:1021592817039http://dx.doi.org/10.1034/j.1600-0706.2002.11824.xhttp://dx.doi.org/10.1007/s10980-005-0063-7http://dx.doi.org/10.1007/s10980-005-0063-7http://dx.doi.org/10.2307/1368643http://dx.doi.org/10.2307/1368643http://dx.doi.org/10.2307/1368643http://dx.doi.org/10.2307/1368643http://dx.doi.org/10.1007/s10980-005-0063-7http://dx.doi.org/10.1007/s10980-005-0063-7http://dx.doi.org/10.1034/j.1600-0706.2002.11824.xhttp://dx.doi.org/10.1023/A:1021592817039http://dx.doi.org/10.1023/A:1021592817039http://dx.doi.org/10.1007/BF00302934http://dx.doi.org/10.1007/BF00302934http://dx.doi.org/10.1046/j.1523-1739.1995.09061466.xhttp://dx.doi.org/10.1126/science.1133097http://dx.doi.org/10.1146/annurev.ecolsys.34.011802.132419http://dx.doi.org/10.1017/S1464793105006949http://dx.doi.org/10.1017/S1464793105006949http://dx.doi.org/10.1017/S0266467400001255http://dx.doi.org/10.2307/1943247http://dx.doi.org/10.1111/j.0030-1299.2004.12524.xhttp://dx.doi.org/10.1111/j.0030-1299.2004.12524.xhttp://dx.doi.org/10.1016/S0169-2046(97)00017-0http://dx.doi.org/10.1016/S0169-2046(97)00017-0http://dx.doi.org/10.1016/j.biocon.2004.03.033http://dx.doi.org/10.1111/j.0906-7590.2004.03764.xhttp://dx.doi.org/10.1071/WR00012http://dx.doi.org/10.1111/j.1474-919X.1983.tb03150.xhttp://dx.doi.org/10.1111/j.1474-919X.1983.tb03150.xhttp://dx.doi.org/10.1111/j.1744-7429.2008.00409.xhttp://dx.doi.org/10.1590/S0001-37652006000400003http://www.teses.usp.br/teses/disponiveis/41/41134/tde-13022008-180423/http://www.teses.usp.br/teses/disponiveis/41/41134/tde-13022008-180423/http://www.teses.usp.br/teses/disponiveis/41/41134/tde-13022008-180423/7/30/2019 Boscolo&Metzger 2009 LandsEcol
11/12
Hanski I (1994) A practical model of metapopulation dynam-
ics. J Anim Ecol 63:151162. doi:10.2307/5591
Harrell FE (2001) Regression modeling strategies. Springer,
Berlin
Heinz SK, Conradt L, Wissel C et al (2005) Dispersal behav-
iour in fragmented landscapes: deriving a practical for-
mula for patch accessibility. Landscape Ecol 20:8399.
doi:10.1007/s10980-004-0678-0
Jaquet N (1996) How spatial and temporal scales influence
understanding of Sperm Whale distribution: a review.
Mammal Rev 26:5165. doi:10.1111/j.1365-2907.1996.
tb00146.x
Lawler JJ, Edwards TC Jr (2002) Landscape patterns as habitat
predictors: building and testing models for cavity-nesting
birds in the Uinta Mountains of Utah, USA. Landscape
Ecol 17:233245. doi:10.1023/A:1020219914926
Levin SA (1992) The problem of pattern and scale in ecology.
Ecology 73:19431967. doi:10.2307/1941447
Li H, Wu J (2007) Landscape pattern analysis: key issues and
challenges. In: Wu J, Hobbs RJ (eds) Key topics in
landscape ecology. Cambridge University Press, Cam-
bridge, pp 3961Lindenmayer DB (2000) Factors at multiple scales affecting
distribution patterns and their implications for animal
conservationLeadbeaters Possum as a case study. Bio-
divers Conserv 9:1535. doi:10.1023/A:1008943713765
Lindenmayer DB, McCarthy MA, Pope ML (1999) Arboreal
marsupial incidence in eucalypt patches in south-eastren
Australia: a test of Hanskis incidence function meta-
population model for patch occupancy. Oikos 84:99109.
doi:10.2307/3546870
Major RE, Christie FJ, Gowing G et al (1999) Age structure
and density of red-capped robin populations vary with
habitat size and shape. J Appl Ecol 36:901908. doi:
10.1046/j.1365-2664.1999.00457.x
Maldonado-Coelho M, Marini MA (2000) Effects of forestfragment size and successional stage on mixed-species bird
flocks in southeastern Brazil. Condor 102:585594. doi:
10.1650/0010-5422(2000)102[0585:EOFFSA]2.0.CO;2
Manning AD, Lindenmayer BD, Barry SC et al (2006) Multi-
scale site and landscape effects on the vulnerable superb
parrot of south-eastern Australia during the breeding
season. Landscape Ecol 21:11191133. doi:10.1007/
s10980-006-7248-6
Martensen AC, Pimentel RG, Metzger JP (2008) Relative
effects of fragment size and connectivity on bird com-
munity in the Atlantic rain forest: implications for con-
servation. Biol Conserv 141:21842192. doi:10.1016/
j.biocon.2008.06.008
Martnez JA, Serrano D, Zuberogoitia I (2003) Predictive
models of habitat preferences for the Eurasian eagle owl
Bubo bubo: a multiscale approach. Ecography 26:2128.
doi:10.1034/j.1600-0587.2003.03368.x
Mazerolle MJ, Villard MA (1999) Patch characteristics and
landscape context as predictors of species presence and
abundance: a review. Ecoscience 6:117124
McGarigal K, Marks BJ (1995) FRAGSTATS: spatial pattern
analysis program for quantifying landscape structure.
USDA For. Serv. Gen. Tech. Rep. PNW-351
Melo-Junior TA, Vasconcelos MF, Fernandes W et al (2001)
Bird species distribution and conservation in serra do
cipo, Minas Gerais, Brazil. Bird Life Int 11:189204
Metzger JP (1998) Estrutura da paisagem e fragmentacao:
analise bibliografica. An Acad Bras Cienc 71:445463
Metzger JP, Decamps H (1997) The structural connectivity
threshold: an hypothesis in conservation biology at the
landscape scale. Acta Ecol 18:112
Meyer CB, Thuiller W (2006) Accuracy of resource selection
functions across spatial scales. Divers Distrib 12:288297.
doi:10.1111/j.1366-9516.2006.00241.x
Neel MC, McGarigal K, Cushman SA (2004) Behavior of
class-level landscape metrics across gradients of class
aggregation and area. Landscape Ecol 19:435455. doi:
10.1023/B:LAND.0000030521.19856.cb
Oliveira-Filho AT, Fontes MAL (2000) Patterns of floristic
differentiation among Atlantic Forests in southeastern
Brazil and influence of climate. Biotropica 32:793810
R Development Core Team (2005) R: a language and envi-
ronment for statistical computing. R Foundation for Sta-
tistical Computing, Vienna
Rahbek C, Graves GR (2001) Multiscale assessment of patternsof avian species richness. Proc Natl Acad Sci USA
98:45344539. doi:10.1073/pnas.071034898
Renfrew RB, Ribic CA (2008) Multi-scale models of grassland
passerine abundance in a fragmented system in Wiscon-
sin. Landscape Ecol 23:181193. doi:10.1007/s10980-
007-9179-2
Ribeiro MC, Metzger JP, Martensen AC, Ponzoni FJ, Hirota
MM (2009) Brazilian Atlantic forest: how much is left and
how is the remaining forest distributed? Implications for
conservation. Biol Conserv 142:11411153. doi:10.1016/
j.biocon.2009.02.021
Roberts DL, Cooper RJ, Petit LJ (2000) Use of premontane
moist forest and shade coffe agroecosystems by army ants
in western Panama. Conserv Biol 14:192199. doi:10.1046/j.1523-1739.2000.98522.x
Ross JLS, Moroz IC (1997) Mapa Geomorfologico do Estado
de Sao Paulo, escala 1:500.000. Volume 1. Geografia-
FFLCH-USP, IPT and Fapesp, Sao Paulo
Sekercioglu CH, Ehrlich PR, Daily GC et al (2002) Disap-
pearance of insectivorous birds from tropical forest frag-
ments. Proc Natl Acad Sci USA 99:263267. doi:10.1073/
pnas.012616199
Sick H (1997) Ornitologia Brasileira. Editora Nova Fronteira,
Rio de Janeiro
Snow DW (1976) The web of adaptation. Quadrangle/The New
York Times Book Co., New York
Soares ES, dos Anjos L (1999) Efeito da fragmentacao florestal
sobre aves escaladoras de tronco e galho na regiao de
Londrina, norte do estado do Parana, Brasil. Ornitol
Neotrop 10:6168
Stotz DF, Fitzpatrick JW, Parker TAIII, Moskovits DK (1996)
Neotropical birds: ecology and conservation. The Uni-
versity of Chicago Press, Chicago
Stratford JA, Stouffer PC (1999) Local extinctions of terrestrial
insectivorous birds in a fragmented landscape near Man-
aus, Brazil. Conserv Biol 13:14161423. doi:10.1046/
j.1523-1739.1999.98494.x
Landscape Ecol (2009) 24:907918 917
123
http://dx.doi.org/10.2307/5591http://dx.doi.org/10.1007/s10980-004-0678-0http://dx.doi.org/10.1111/j.1365-2907.1996.tb00146.xhttp://dx.doi.org/10.1111/j.1365-2907.1996.tb00146.xhttp://dx.doi.org/10.1023/A:1020219914926http://dx.doi.org/10.2307/1941447http://dx.doi.org/10.1023/A:1008943713765http://dx.doi.org/10.2307/3546870http://dx.doi.org/10.1046/j.1365-2664.1999.00457.xhttp://dx.doi.org/10.1650/0010-5422(2000)102[0585:EOFFSA]2.0.CO;2http://dx.doi.org/10.1007/s10980-006-7248-6http://dx.doi.org/10.1007/s10980-006-7248-6http://dx.doi.org/10.1016/j.biocon.2008.06.008http://dx.doi.org/10.1016/j.biocon.2008.06.008http://dx.doi.org/10.1034/j.1600-0587.2003.03368.xhttp://dx.doi.org/10.1111/j.1366-9516.2006.00241.xhttp://dx.doi.org/10.1023/B:LAND.0000030521.19856.cbhttp://dx.doi.org/10.1073/pnas.071034898http://dx.doi.org/10.1007/s10980-007-9179-2http://dx.doi.org/10.1007/s10980-007-9179-2http://dx.doi.org/10.1016/j.biocon.2009.02.021http://dx.doi.org/10.1016/j.biocon.2009.02.021http://dx.doi.org/10.1046/j.1523-1739.2000.98522.xhttp://dx.doi.org/10.1073/pnas.012616199http://dx.doi.org/10.1073/pnas.012616199http://dx.doi.org/10.1046/j.1523-1739.1999.98494.xhttp://dx.doi.org/10.1046/j.1523-1739.1999.98494.xhttp://dx.doi.org/10.1046/j.1523-1739.1999.98494.xhttp://dx.doi.org/10.1046/j.1523-1739.1999.98494.xhttp://dx.doi.org/10.1073/pnas.012616199http://dx.doi.org/10.1073/pnas.012616199http://dx.doi.org/10.1046/j.1523-1739.2000.98522.xhttp://dx.doi.org/10.1016/j.biocon.2009.02.021http://dx.doi.org/10.1016/j.biocon.2009.02.021http://dx.doi.org/10.1007/s10980-007-9179-2http://dx.doi.org/10.1007/s10980-007-9179-2http://dx.doi.org/10.1073/pnas.071034898http://dx.doi.org/10.1023/B:LAND.0000030521.19856.cbhttp://dx.doi.org/10.1111/j.1366-9516.2006.00241.xhttp://dx.doi.org/10.1034/j.1600-0587.2003.03368.xhttp://dx.doi.org/10.1016/j.biocon.2008.06.008http://dx.doi.org/10.1016/j.biocon.2008.06.008http://dx.doi.org/10.1007/s10980-006-7248-6http://dx.doi.org/10.1007/s10980-006-7248-6http://dx.doi.org/10.1650/0010-5422(2000)102[0585:EOFFSA]2.0.CO;2http://dx.doi.org/10.1046/j.1365-2664.1999.00457.xhttp://dx.doi.org/10.2307/3546870http://dx.doi.org/10.1023/A:1008943713765http://dx.doi.org/10.2307/1941447http://dx.doi.org/10.1023/A:1020219914926http://dx.doi.org/10.1111/j.1365-2907.1996.tb00146.xhttp://dx.doi.org/10.1111/j.1365-2907.1996.tb00146.xhttp://dx.doi.org/10.1007/s10980-004-0678-0http://dx.doi.org/10.2307/55917/30/2019 Boscolo&Metzger 2009 LandsEcol
12/12
Taylor PD, Fahrig L, Henein K et al (1993) Connectivity is a
vital element of landscape structure. Oikos 68:571573.
doi:10.2307/3544927
Thompson WL (2002) Towards reliable bird surveys: account-
ing for individuals present but not detected. Auk 119:
1825. doi:10.1642/0004-8038(2002)119[0018:TRBSAF]
2.0.CO;2
Thompson CM, McGarigal K (2002) The influence of research
scale on bald eagle habitat selection along the lower
Hudson River, New York (USA). Landscape Ecol
17:569586. doi:10.1023/A:1021501231182
Uezu A, Metzger JP, Vielliard JME (2005) Effects of structural
and functional connectivity and patch size on the abun-
dance of seven Atlantic Forest bird species. Biol Conserv
123:507519. doi:10.1016/j.biocon.2005.01.001
van Rensburg BJ, Chown SL, Gaston KJ (2002) Species
richness, environmental correlates, and spatial scale: a test
using South African birds. Am Nat 159:566577. doi:
10.1086/339464
Vergara PM, Armesto JJ (2009) Responses of Chilean forest
birds to anthropogenic habitat fragmentation across spatial
scales. Landscape Ecol 24:2538. doi:10.1007/s10980-
008-9275-y
Wiens JA (1989) Spatial scaling in ecology. Funct Ecol 3:385
397. doi:10.2307/2389612
Wiens JA (1995) Habitat fragmentation: island v landscape
perspectives on bird conservation. Ibis 137:97104. doi:
10.1111/j.1474-919X.1995.tb08464.x
Willis EO, Oniki Y (1978) Birds and army ants. Annu Rev
Ecol Syst 9:243263. doi:10.1146/annurev.es.09.110178.
001331
Wu J (2007) Scale and scaling: a cross-disciplinary perspective.
In: Wu J, Hobbs RJ (eds) Key topics in landscape ecology.
Cambridge University Press, Cambridge, pp 115142
918 Landscape Ecol (2009) 24:907918
123
http://dx.doi.org/10.2307/3544927http://dx.doi.org/10.1642/0004-8038(2002)119[0018:TRBSAF]2.0.CO;2http://dx.doi.org/10.1642/0004-8038(2002)119[0018:TRBSAF]2.0.CO;2http://dx.doi.org/10.1023/A:1021501231182http://dx.doi.org/10.1016/j.biocon.2005.01.001http://dx.doi.org/10.1086/339464http://dx.doi.org/10.1007/s10980-008-9275-yhttp://dx.doi.org/10.1007/s10980-008-9275-yhttp://dx.doi.org/10.2307/2389612http://dx.doi.org/10.1111/j.1474-919X.1995.tb08464.xhttp://dx.doi.org/10.1146/annurev.es.09.110178.001331http://dx.doi.org/10.1146/annurev.es.09.110178.001331http://dx.doi.org/10.1146/annurev.es.09.110178.001331http://dx.doi.org/10.1146/annurev.es.09.110178.001331http://dx.doi.org/10.1111/j.1474-919X.1995.tb08464.xhttp://dx.doi.org/10.2307/2389612http://dx.doi.org/10.1007/s10980-008-9275-yhttp://dx.doi.org/10.1007/s10980-008-9275-yhttp://dx.doi.org/10.1086/339464http://dx.doi.org/10.1016/j.biocon.2005.01.001http://dx.doi.org/10.1023/A:1021501231182http://dx.doi.org/10.1642/0004-8038(2002)119[0018:TRBSAF]2.0.CO;2http://dx.doi.org/10.1642/0004-8038(2002)119[0018:TRBSAF]2.0.CO;2http://dx.doi.org/10.2307/3544927