Supplementary material:
The following supplementary material is available for this article online
Appendix S1:Vegetation characteristics measured at plots
At each point-count station I measured vegetation after the bird surveys to determine if there was
any bias in treatment applications such that differences in habitat structure could explain patterns
of habitat use. To do so, I estimated metrics related to canopy, shrub, and ground cover.
Vegetation was measured at 4 sampling locations within each point-count area: one at the center
of the count and three at locations 25 m from the center, at 0, 120, and 240. At each sampling
location I measured vegetation composition and structure within 5-m and 11.3-m radii. Within
the 5-m radius, I visually estimated shrub cover (by species) and ground cover (< 1 m) structure,
based on overlapping ocular percentages. Ground cover categories included woody, grass, forb,
litter, and bare ground. Within the 11.3-m radius, I counted the total number of trees (by species)
and snags by size, based on three diameter breast height (dbh) size categories: 8-23, 23-38 cm,
and >38 cm. I also measured canopy height (using a clinometer) and estimated canopy cover by
averaging 4 densiometer readings (one in each cardinal direction). I tested for differences in
habitat structure among treatments using MANOVA. I focused on vegetation metrics reflecting
variation in shrub and tree/canopy structure, based on known habitat associations and nest-site
locations of each species. Response variables included total shrub cover, shrub diversity
(Simpson index), canopy height, canopy cover, tree diversity, and the density of deciduous trees
(total number of trees/ha), conifer trees, and snags. Overall, there was no evidence for
differences in vegetation structure among plot types (Table S1; Wilk’s  = 0·56, P = 0·84).
Table S1. Vegetation structure in plots with added least flycatcher cues, American redstart cues,
and controls, 2003-2005.
Control
Vegetation characteristic
Mean
Flycatcher
SE
Mean
SE
Redstart
Mean
SE
Tree and canopy:
Canopy cover (%)
47·6
5·7
40·4
7·1
51·1
7·1
Canopy height (m)
11·1
1·6
8·8
2·0
10·6
2·0
Deciduous trees (number/ha)
114·2
28·1
59·8
35·2
81·3
35·2
Coniferous trees (number/ha)
16·5
11·2
12·5
14·0
8·9
14·0
Tree diversity†
0·36
0·07
0·35
0·09
0·37
0·09
4·9
1·8
1·8
2.2
4·5
2·2
Total shrub cover (%)
44·2
6·7
37·6
8·3
62·9
8·3
Shrub diversity†
0·52
0·05
0·49
0·06
0·62
0·06
Snags (number/ha)
Shrubs:
†Simpson index (1 – D)
Appendix S2: Estimation of detection probability
Based on point counts during the breeding season surveys, I estimated bird density (birds/ha) by
correcting raw counts with estimated detection probabilities, which were estimated using a
removal model in Program MARK (Farnsworth et al. 2002; Moore, Scheiman & Swihart 2004).
Removal models estimate detection probabilities by considering an individual point count as a
closed population, in which an individual is “removed” once it is detected, based on time
intervals within each point count survey. Note that this approach does not assume that the
population is closed for different surveys. I considered the following potential covariates that
could influence detection probabilities: treatment, observer (five observers collected data across
the three years), and sex (singing males versus other). I considered those covariates because the
addition of song playbacks could influence singing rates of birds, observers could vary in their
ability to detect individuals of different species, and males are typically easier to detect based on
singing than other individuals not vocalizing. I compared a global model—p(observer +
treatment + sex)—to reduced models using Akaike’s Information Criterion (adjusted for sample
size; AICc) and AICc model weights (Hurvich & Tsai 1989; Burnham & Anderson 1998), which
is a useful method for selecting models for parameter estimation (Johnson & Omland 2004).
Interactions among covariates were not used because of limited detections for some covariate
combinations.
References for the Appendix S1
Burnham, K.P. & Anderson, D.R. (1998) Model selection and inference: a practical
information-theoretic approach Springer-Verlag, New York.
Farnsworth, G.L., Pollock, K.H., Nichols, J.D., Simons, T.R., Hines, J.E., & Sauer, J.R. (2002) A
removal model for estimating detection probabilities from point-count surveys. Auk, 119,
414-425.
Hurvich, C.M. & Tsai, C.L. (1989) Regression and time series model selection in small samples.
Biometrika, 76, 297-307.
Johnson, J.B. & Omland, K.S. (2004) Model selection in ecology and evolution. Trends in
Ecology & Evolution, 19, 101-108.
Moore, J.E., Scheiman, D.M., & Swihart, R.K. (2004) Field comparison of removal and
modified double-observer modeling for estimating detectability and abundance of birds.
Auk, 121, 865-876.
Table S2. A comparison of removal models that incorporate different covariates for estimating
detection probabilities of least flycatchers, based on the relative Akaike’s Information Criterion
(adjusted for sample size; AICc = AICci - minAICc) and AICc model weights (wi).
AICc
AICc
wi
K‡
p(.)
356·29
0·00
0·39
1
p(sex)
357·23
0·95
0·24
2
p(trt)
359·17
2·89
0·09
3
p(obs)
359·22
2·93
0·09
5
p(trt + sex)
360·13
3·84
0·06
4
p(obs + sex)
360·17
3·88
0·06
6
p(obs + trt)
360·40
4·11
0·05
7
p(obs + trt + sex)
361·31
5·03
0·03
8
Model†
†p = detection probability, sex = singing males versus other, trt = treatment, obs = observer (5
total observers throughout study)
‡
K = number of parameters in the model.