boscolo&metzger 2009 landsecol

7/30/2019 Boscolo&Metzger 2009 LandsEcol

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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


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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

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(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

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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

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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

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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

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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

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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


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.

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