Early-Successional Breeding Bird Communities in
Intensively Managed Pine Plantations: Influence of
Vegetation Succession but Not Site Preparations
Falyn L. Owens, Philip C Stouffer, Michael J. Chamberlain, and Darren A. Miller
Southeastern Naturalist, Volume 13, Issue 3 (2014): 423–443
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22001144 SOUTHEASTERN NATURALIST 1V3o(3l.) :1432,3 N–4o4. 33
Early-Successional Breeding Bird Communities in
Intensively Managed Pine Plantations: Influence of
Vegetation Succession but Not Site Preparations
Falyn L. Owens1, Philip C Stouffer1,*, Michael J. Chamberlain1, 2,
and Darren A. Miller3
Abstract - Birds that require early-successional habitat are declining in North America due
to habitat loss. Their increasing reliance on anthropogenic landscapes, such as the extensive
Pinus spp. (pine) plantations of the southeastern US, makes it important to assess how management
alternatives within these forests influence habitat quality. We examined how 2 site
preparation variables, tree row spacing (4.3 m vs. 6.1 m) and arrangement of post-harvest
woody debris (piled vs. scattered), influenced species richness, abundance, and breeding
activity of disturbance-dependent (early-successional) birds. We studied bird communities
and vegetation structure during the first 5 years of growth on replicated plots in 4 intensively
managed Pinus taeda (Loblolly Pine ) plantations in Louisiana. We used model selection to
determine which site-preparation and vegetation characteristics most influenced avian communities.
All measures of bird communities responded positively as vegetation structure
and cover increased over time. However, neither row spacing nor debris placement affected
vegetation variables important to birds for at least for the first 5 years following stand establishment;
bird communities responded to successional changes and variation among plots,
but not to site preparation. Land managers seeking to provide early-successional habitat in
recently established plantations for disturbance-dependent birds can do so by increasing
structural complexity and groundcover through selective herbicide applications, mechanical
treatments, or other means.
Introduction
Birds that live exclusively in early-successional landscapes are among the most
threatened avian habitat specialists in North America. Fifty-six percent of grassland
species, 39% of shrub-scrub species, and 33% of savannah species have experienced
significant declines in the last 45 years (Brawn et al. 2001, North American
Bird Conservation Initiative 2009). As of 2011, breeding bird survey data from
the US and Canada show declining population trends for 44% of successional or
scrub-breeding species, with increases for only 9% of these 160 species (Sauer et
al. 2013). Historically, these species preferred habitat conditions perpetuated with
regular burning via natural and anthropogenic origins, which prevented encroachment
of woody vegetation and maintained habitat structure in a sub-climactic
successional state. Today, those habitat types have been drastically reduced by
1School of Renewable Natural Resources, Louisiana State University Agricultural Center, Baton
Rouge, LA, 70803. 2Current address - Warnell School of Forestry and Natural Resources,
University of Georgia, Athens, GA 30602. 3Southern Timberlands Technology, Weyerhaeuser
Company, Columbus, MS, 39704.*Corresponding author - pstouffer@lsu.edu.
Manuscript Editor: Paul Leberg
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spread of human-dominated landscapes, fire suppression, and lack of active timber
harvest (Beissinger et al. 2000, North American Bird Conservation Initiative 2009,
Trani et al. 2001; see Twedt et al. 1999 for a summary of trends in the Mississippi
Alluvial Valley).
Young, intensively managed Pinus spp. (pine) plantations provide habitat
conditions that can support more birds than historically disturbed landscapes
(Brawn et al. 2001, DeGraaf 1991, Dickson et al. 1995, Keller et al. 2003,
Thompson et al. 1993). Where timber is an important industry, pine plantations
can provide a significant amount of disturbed habitat. In the southeastern US,
pine plantations account for 20% of forest cover, with Pinus taeda L. (Loblolly
Pine) plantations covering 13.4 million ha (Schultz 1997, US Forest Service
2008). The economic value of plantations helps prevent land conversion to more
intensive anthropogenic uses, making them important refugia for disturbancedependent
birds (Brawn et al. 2001, North American Bird Conservation Initiative
2009). Timber managers’ decisions about how they manage their forests, including
row spacing and handling of coarse woody debris (CWD), affect timber
production, production costs, and value for wildlife.
To understand and improve bird resources in intensively managed timberlands,
experimental studies are required to determine influences of different management
practices, how they interact to affect birds, and how these patterns change by geographic
region (Brawn et al. 2001, Hanberry et al. 2012, Kilgo et al. 2000, Miller
et al. 2009). Research has shown that sites where CWD is left after timber harvest
support as much as 45% more bird species at 50% higher densities compared to
stands where debris is shredded or removed (Horn 2000; Jones et al. 2009a, b; Lohr
et al. 2002). If woody debris remains on-site, timber managers have the choice of
spreading it between newly planted rows or creating debris piles; this decision may
also influence habitat quality for birds.
Similarly, row spacing in planted stands may affect habitat quality for birds. Weyerhaeuser
Company, one of the largest industrial landowners in the southeastern US,
recently switched row spacing from 4.3 m to 6.1 m. In Georgia, Lane et al. (2011)
found that wider spacing between rows (6.1 m vs. 3.0 m) improved species richness
and abundance, but the study confounded row spacing with debris management; data
are limited relative to tree spacing and avian habitat quality. Therefore, we examined
how breeding bird communities responded to both row spacing (4.3 m vs. 6.1 m) and
woody debris placement (piled vs. scattered) treatments in regenerating Loblolly
Pine plantations in Louisiana. Vegetation structure and diversity have been examined
on the same plots where we studied birds; neither plant diversity nor species richness
was influenced by debris placement or row spacing (Grace et al. 2011).
Our specific research objectives were to: (1) evaluate debris placement and rowspacing
effects on breeding bird species richness, abundance, and breeding activity
during the first 5 years after stand establishment; (2) investigate the influence of
changing vegetation composition and structure on breeding bird communities during
the 5 years following stand establishment; (3) identify vegetation response to
site preparation, which may represent indirect influences of preparation treatments
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on breeding bird communities; and (4) recommend which combination of row spacing
and debris placement provides the most benefit to early-successional breeding
bird communities.
Methods
Site characteristics and experimental design
We conducted our study in Louisiana on 4 sites owned and managed by Weyerhaeuser
Company (hereafter Weyerhaeuser), the largest industrial landowner in
that state. Two sites were in the north-central part of the state, in Winn (site A) and
Jackson (site B) parishes, and 2 were in southeastern Louisiana, in Tangipahoa (site
C) and Washington (site D) parishes (figure 1 in Owens 2011). Weyerhaeuser managed
these sites for production of Loblolly Pine saw-timber. We established our study
plots following standard industry practices (D.A. Miller, pers. observ.). Typical silviculture
on this area included clearcut harvest at approximately 27–32 years of age,
followed by site preparation and planting (~1100 trees/ha), vegetation management,
a commercial thinning (target reduction to ~309 trees/ha), pruning, and fertilization.
The study plots at each site were established within a single large clearcut, which
in turn was embedded within a much larger area of managed Loblolly Pine forest.
Small portions of most sites were designated streamside management zones (SMZs),
where undisturbed forested vegetation, primarily mature hardwood stands, bordered
watercourses. Sites shared similar annual precipitation, temperature, elevation, and
soil characteristics; soil drainage ranged from poorly (site C) to well-drained (site D)
(Natural Resources Conservation Service 2009, Owens 2011).
Following harvest, sites were prepared for planting with a combination of mechanical
and chemical treatments tailored to achieve successful regeneration at
each site as per Weyerhaeuser standard procedures. After site preparation, trees
were planted during winter 2005–2006. To allow us to examine effects of alternative
industry choices for row spacing and debris treatments, managers established
experimental stands in a randomized block design to compare all combinations of
woody debris placement (piled or scattered) and row spacing (4.3 m vs. 6.1 m) in
four 10-ha plots at each of the 4 sites (Fig. 1). In piled plots, plantation managers
mechanically raked debris into 5 large piles (mean ± SE: 21.7 m ± 11.2 m x 17.2 m
± 5.0 m x 3.6 m ± 0.8 m [Bechard 2008]), which they placed in the center and near
the corners of the plots. Scattered sections had woody debris distributed between
rows throughout the plot; the rows of trees were elevated onto soil beds to reduce
inundation of seedlings and prepare a good planting substrate. To temporarily
reduce competing vegetation, all sites received a combination banded application
(i.e., herbicide only applied to beds of planted tress) of the herbicides Arsenal® AC
(4 oz/ac, BASF Corp. Research Triangle Park, NC) and Oust Extra® (2.5 oz/ac,
DuPont™ Crop Protection, Wilmington, DE).
Vegetation sampling
We collected vegetation data during the peak growing season (mid-July) in
2006, 2007, 2009, and 2010. In each 10-ha plot, we established five 10-m-radius
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circular vegetation plots, equally spaced so that they extended diagonally across
the entire length of the 10-ha plot. When necessary, we adjusted plot placement to
avoid debris piles. Stem-count data consisted of total live softwood and hardwood
stems within the plot, excluding stems shorter than 1 m. We measured percent
cover in five 1-m2 subsamples using a Daubenmire frame (Daubenmire 1959), and
categorized vegetation as either fern, Ilex vomitoria Aiton (Yaupon), forb, vine,
woody, grass, debris, or bare ground. Due to layering of vegetation, total percent
cover exceeded 100%. Yaupon received its own category because of its prevalence
and uniquely dense structure. We recorded maximum vegetation height (m), average
height (m), and vertical obstruction by reading a centrally placed, 1.5-m Robel
pole from 10 m away in each cardinal direction (Robel et al. 1970). We obtained
13 different measures of composition and structure on 80 vegetation plots each
year. To simplify analyses, we averaged measurements to the vegetation-plot level,
yielding 40 observations per individual treatment per year (or 20 observations per
treatment combination per year).
Avian community sampling
We surveyed breeding bird communities for 4 breeding seasons. We initially surveyed
the northernmost sites (A and B) during 2006 directly following plantation
establishment. We surveyed all sites in 2007, 2009, and 2010. We surveyed plots
5 times each year between late April and early June, during the peak of breeding
activity, with gaps of at least 10 days between surveys to increase temporal independence
in our data. We did not survey on rainy, windy, or heavily overcast days
(following Hamel et al. 1996).
Figure 1. Factorial arrangement of row spacing (4.3 m or 6.1 m) and debris placement
(scattered or piled) on plots in four Loblolly Pine stands established in 2005 in Louisiana.
Circles represent locations of debris piles on piled plots and avian survey-points on all plots.
Orientation of plots in relation to one another is not necessarily accurate because plot position
was influenced by SMZs and roads.
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Avian surveys consisted of point counts followed by extended searches. We designated
5 point-count locations per plot, corresponding to the center of the plot and
4 corners (Fig. 1). For piled treatment plots, we shifted survey locations the minimum
distance necessary to provide acceptable visibility around debris piles. To
increase sample independence and reduce edge effects, we placed survey locations
≥75 m apart and ≥50 m from plot edges. We based survey procedures on Hamel
et al. (1996), as adapted by the Lower Mississippi Valley Joint Venture (2004). At
each location, during 10-min observation periods, observers noted species, age,
sex, distance and direction from sampling points, and any behaviors indicative of
breeding, such as males defending territory or birds carrying nesting material. We
sampled from 15 min before sunrise until 0900 CDT. We temporally stratified sampling
by reversing survey order for each visit. After completing point counts each
morning, observers conducted extended searches, revisiting each plot for 1 hour
to look for additional evidence of breeding activity. Extended searches typically
finished before 1100 CDT.
We calculated 3 metrics indicative of habitat quality for breeding birds: species
richness, abundance, and breeding activity. We excluded non-breeding winter residents,
passage migrants, flyovers, and species with territories larger than a single
plot, such as raptors. We also excluded birds primarily residing in SMZs; these species
were forest interior and edge specialists that were not present in plots without
SMZs. We calculated species richness as number of unique species observed over
all point counts and extended searches in a given year for each plot. We accounted
for variation in detectability among species by adjusting raw species richness with
the program SPECRICH2, which estimates number of species present even if not
all are detected, assuming that individual species vary in detectability (Hines 1996,
White et al. 1978).
For abundance, we determined mean number of individuals per species per
survey. We assumed that on some days we did not detect all individuals in a plot
and sometimes we detected more individuals than actually used the plots all season
(during territory establishment). Therefore, we reported mean abundance to account
for this variation. We summed mean abundances over all detected species,
yielding total abundance per plot per year. It is important to note that abundance
values were valid only relative to each other and should not be used as absolute
measures in comparisons outside this study (MacKenzie and Kenda ll 2002).
We included a measure of breeding activity in our analyses to address the potential
problem of birds using plots without breeding (Brawn et al. 2001, Van Horne
1983). We used an index modified from Vickery et al. (1992) that assigns scores
to breeding territories based on strength of evidence that young have successfully
fledged from them (Table 1). We gave partial scores to territories with males present
for as few as 2 weeks to account for territories that may have been active, but undetected,
for a longer period. This method is limited to non-cryptic species whose
young are altricial, and whose breeding behaviors are relatively detectable (Rangen
et al. 2000, Rivers et al. 2003). For this reason, we excluded Colinus virginianus (L.)
(Northern Bobwhite ) and Archilochus colubris (L.) (Ruby-throated Hummingbird)
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from breeding-activity measures. We also excluded the brood parasite Molothrus
ater (Boddaert) (Brown-headed Cowbird). The final score for each territory was the
greatest evidence from that territory over a season, and the final score for each plot
was the sum of territory scores for all species with territories in the plot that year.
Statistical analysis
We used principal component analysis (PCA) to reduce correlation in the 13
vegetation metrics, employing Cattell’s scree test and the Kaiser-Guttman criterion
to identify and retain components that explained the most variance and were most
biologically meaningful (PROC FACTOR; Jackson 1993, SAS 2008). We used
VARIMAX rotation to increase interpretability of retained components and primarily
considered correlations >|0.35| when determining their meaning. Grace et
al. (2011) conducted a similar analysis of the vegetation data, but our analysis also
included pine stem counts to account for variation in pine seedling survival in the
face of insignificant treatment effects of row spacing (Grace et al. 201 1).
We used analyses of variance (ANOVA) with repeated measures to test the
null hypothesis that avian community metrics, vegetation components, and
breeding activity for select species designated by the USFWS (2008) as being of
conservation concern (2009 and 2010 only) did not differ among row spacing and
debris-distribution treatments. Predictor variables were row spacing, debris placement,
and their interaction (fixed), with site as the blocked random effect and year
as the repeated measure (PROC MIXED; SAS 2008). We specified autoregressive
covariance and the Kenward-Rogers adjustment for degrees of freedom (Kenward
and Roger 1997, Kowalchuk et al. 2004). We obtained parameter estimates using
maximum likelihood estimation and used Tukey-adjusted P-values. We assessed
normality by examining skewness, kurtosis, normal probability plots, and Shapiro-
Wilk test results, transforming data when necessary to meet normality assumptions.
We also tested response of bird communities and vegetation to year alone, using
similar specifications but no repeated measures.
To describe patterns in bird-community data, we first examined yearly and
overall correlations among the three community metrics. We then used AIC-based
model selection for 14 candidate models that included debris-placement treatment,
vegetation components, and their interactions in combinations that could be biologically
meaningful (Table 2; Burnham and Anderson 2002). We reasoned that
Table 1. Breeding-activity ranks, modified from Vickery et al. (1992). Territorial refers to males who
sing, monitor a distinct area, or act aggressively toward other males.
Score Breeding behavior
0.33 Territorial male present 2 weeks
0.66 Territorial male present 3 weeks
1 Territorial male present 4+ weeks
2 Territorial male and female present 4+ weeks
3 Adults with nesting material, laying or incubating eggs, or diverting attention from nest
4 Adults carrying food or fecal sacs
5 Juveniles present
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row spacing was represented by proxy in the vegetation data (pine stem counts);
this variable was also uninformative in a prior analysis of vegetation diversity on
these plots (Grace et al. 2011), so we excluded it from all models. With individual
avian community measures specified as response variables, we tested each model as
analyses of covariance (ANCOVA) with repeated measures (PROC MIXED; SAS
2008). We evaluated global models using the chi-square goodness-of-fit statistic.
Using these ANCOVA models, we considered bird-community responses to debris
placement and vegetation components from best-fit models (lowest AICC) and competitive
models (ΔAICC < 4) (Burnham and Anderson 2002).
Results
Vegetation summary
We detected 124 separate plant taxa (genera or species) in vegetation sampling
plots. Although Loblolly Pine was the only softwood species, sites hosted an array
of hardwoods, including natives Liquidambar styraciflua L. (Sweetgum), Sassafras
albidum (Nuttall) (Sassafras), Acer rubrum L. (Red Maple), Yaupon, Rhus copallina
L. (Winged Sumac), and Quercus spp. (oaks), and the non-natives Ligustrum
sinense Lour. (Chinese Privet) and Triadica sebifera (L.) Small (Chinese Tallow).
A patchy shrub layer was dominated by Callicarpa americana L. (American Beautyberry),
Baccharis halimifolia L. (Eastern Baccharis), and Cyrilla racemiflora L.
(Swamp Titi), interwoven with abundant canes of Rubus spp. (blackberries) and
some Smilax spp. (greenbriers). Dominant grasses belonged to genera Andropogon
and Schizachyrium spp. (bluestem grasses); Solidago spp. (goldenrods), Eupatorium
spp. (Joe-pye weeds), Ambrosia spp. (ragweeds), and Aster spp. (asters) were
Table 2. Set of candidate models used in AIC-based model selection to determine the response of
breeding avian communities to debris placement and vegetation characteristics in four young Loblolly
Pine stands in 2006, 2007, 2009, and 2010 in Louisiana. Variables include debris placement and 3
principal components describing vegetation structure (Table 3).
Variables
Vegetation Evergreen Groundcover
Model Debris (D) structure (S) cover (E) (G) Interactions
GLOBAL D S E G D*S D*E S*E
D S E G
D S E
D S D*S
D S
D E D*E
D E
S E G S*E
S E G
S E S*E
S E
S
E
NULL
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the most common forbs. Differences in soil drainage among sites were reflected
in species composition—the wettest site (C) was dominated by lowland hardwood
and freshwater marsh species (Nyssa spp. and Saururus cernuus L. [Lizard’s Tail])
and the driest site (D) was characterized by species associated with upland areas
(Yaupon and bluestem grasses).
Principal component analysis of the 13 vegetation measures (pine and hardwood
stem counts, percent groundcover categories, and height measures) yielded
3 composite variables that satisfied retention requirements. We interpreted them as
overall vegetation structure, evergreen cover, and groundcover (Table 3). Vegetation
structure (Eigenvalue = 4.55) represented a gradient between bare ground and
tall, dense vegetation, mostly encompassing the variation in hardwoods. Evergreen
cover (Eigenvalue = 1.74) represented a gradient in cover between the 2 evergreen
species, Loblolly Pine and Yaupon. Groundcover (Eigenvalue = 1.17) represented
a gradient between bare or debris-covered ground and dense grass cover. Although
this component accounted for relatively little variance, we retained it for analysis
because it was the only metric that notably changed directionality through time,
representing the shift from bare ground in 2006 to grassy cover in 2007, and then
back to bare ground as woody species shaded out herbaceous growth in the final
years of our study (Figs. 2, 3). Overall, year was a significant predictor for vegetation
structure (F3, 53 = 12.88, P < 0.001), evergreen cover (F3, 52.5 = 76.04, P < 0.001),
and groundcover (F3, 52.7 = 14.01, P < 0.001). When we tested the influence of site
preparation on vegetation, we found that none of the vegetation metrics varied with
debris placement (F1, 12.3–12.6 < 0.59, P > 0.46), row spacing (F1, 12.3–12.6 < 1.74, P >
0.21), or their interaction (F1, 12.3–12.6 < 0.33, P > 0.58).
Avian community response
We detected 56 bird species, from which we identified 14 (25%) as disturbance-
dependent species based upon their classification as dependent upon
Table 3. Correlations between retained principal components and original vegetation measurements.
A indicates vegetation characteristics that are highly correlated (P > 0.35) with a component.
Vegetation metric PC1-Vegetation structure PC2-Evergreen cover PC3-Groundcover
Pine stem count 0.20 0.81A 0.00
Hardwood stem count 0.64A 0.50A -0.50*
% cover fern 0.19 -0.10 -0.17
% cover Yaupon 0.30 0.78A -0.20
% cover forb 0.18 0.34 0.24
% cover vine 0.49A 0.32 -0.12
% cover woody 0.76A -0.20 -0.90A
% cover grass -0.60A -0.11 0.86A
% cover debris -0.60* -0.26 -0.63*
% cover bare ground -0.54* 0.18 -0.61*
Minimum height 0.74* 0.48* 0.16
Maximum height 0.70* 0.43* 0.33
Average height 0.82* 0.17 0.30
Total variance explained 35.00% 13.40% 9.00%
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grassland, savanna, shrubland, shrubland, or generalized shrub habitats by Askins
(1993) (see Appendix A). The remaining species were habitat generalists, passage
migrants, or late-departing winter residents, or primarily occupied SMZs. Of the
disturbance-dependent breeders, 71% (n = 10) displayed reproductive activity.
We detected 6 species designated as species of conservation concern in the region
they were detected (USFWS 2008), including 4 disturbance-dependent species as
well as Sitta pusilla Latham (Brown-headed Nuthatch) and Icterus spurius (L.)
(Orchard Oriole).
The 4 most common species, accounting for 42% of all detections, were disturbance-
dependent: Icteria virens (L.) (Yellow-breasted Chat ), Passerina cyanea
(L.) (Indigo Bunting), Setophaga discolor (Vieillot) (Prairie Warbler ), and Passerina
caerulea (L.) (Blue Grosbeak). In addition to the disturbance-dependent
species, we irregularly detected some species associated with more developed
forest, such as Poecile carolinensis (Audubon) (Carolina Chickadee), Cyanositta
cristata (L.) (Blue Jay), and Baeolophus bicolor (L.) (Tufted Titmouse).
As expected, species composition changed as stands matured, with ground-foraging
specialists such as Spizella passerina (Bechstein) (Chipping Sparrow) using
plots only in the initial 2 years, and shrub-nesting species like Yellow-breasted Chat
Figure 2. Changes in vegetation structure through time. Lower and upper box edges represent
25th and 75th percentiles; whiskers represent 10th and 90th percentiles. Lines bisecting
boxes represent medians and points signify outliers.
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Figure 3. Changes in evergreen cover and groundcover through time. Lower and upper box
edges represent 25th and 75th percentiles; whiskers represent 10th and 90th percentiles. Lines
bisecting boxes represent medians and points signify outliers.
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and Prairie Warbler appearing later (see Appendix A). Often, species that accounted
for a large percentage of detections in the first years, such as Carolina Wren and
Cardinalis cardinalis (L.) (Northern Cardinal), remained present in comparable
numbers, but declined in proportional presence as species richness and overall
abundance increased through time. Indigo Bunting was unique in maintaining high
relative abundance through all 4 years; it was the most frequently detected species
in 2006 and 2007, and second only to Yellow-breasted Chat in 2009 and 2010.
For all years and plots combined, the three avian community metrics were
strongly correlated (Spearman r2 > 0.41, P < 0.001). For individual years, however,
the relationships among the metrics were less clear, with no significant correlations
in the first or second years (Spearman r2 < 0.27, P > 0.09). By the third year, breeding
score and abundance approached a significant correlation (Spearman r2 = 0.23,
P = 0.051). By year four, estimated species richness and abundance, and abundance
and breeding score were at least weakly correlated (Spearman r2 = 0.19, P < 0.085).
Overall, avian community metrics increased through time, with significant
variation among years for estimated species richness (Figs. 4, 5; F3, 53.4 = 10.9,
P < 0.001), abundance (F3, 52.3 = 71.6, P < 0.001), and breeding activity (F3, 52.3 =
81.1, P < 0.001). Avian communities did not differ based on debris placement
(F1, 15.3–17 < 1.15, P > 0.30), row spacing (F1, 15.3–17 < 0.01, P > 0.93), or their
interaction (F1, 15.3–17 < 1.7, P > 0.21). We had enough data (>20 total detections)
to individually test two species of conservation concern, neither of which showed
a response to these factors: Prairie Warbler (F1, 12 < 0.53, P > 0.48) and Yellowbreasted
Chat (F1, 16 < 3.61, P > 0.08).
Figure 4. Estimated number of species per plot in young Loblolly Pine plantations in Louisiana.
Years represent first (2006), second (2007), fourth (2009), and fifth (2010) breeding
seasons (late April–early June) post-planting. Error bars represent standard error.
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Top models for all bird-community metrics included vegetative structure and
evergreen cover (Table 4). Upon examination of model estimates, we found that
species richness, abundance, and breeding score were positively correlated with
these two measures of vegetation (Table 5). As vegetation became taller, denser,
Figure 5. Abundance and breeding score per plot in young Loblolly Pine plantations in
Louisiana. Years represent first (2006), second (2007), fourth (2009), and fifth (2010)
breeding seasons (late April–early June) post-planting. Error bars represent standard error.
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and more heterogeneous, there was an overall increase in all of our measures of
breeding bird use. In addition, groundcover was influential for all community metrics
except species richness. Although number of species per plot was not positively
correlated with grass cover, more grass cover supported more individuals and more
breeding activity, a logical trend because all of the targeted species use herbaceous
stems for nest building.
Discussion
We found no evidence that avian community response varied between 4.3 m and
6.1-m row spacing, or between piled and scattered debris. Likewise, vegetation structure
and composition, the primary cues for birds searching for breeding territories,
were not influenced by row spacing or debris placement (see also Grace et al. 2011).
Although the evergreen structure component was important for birds and included
pine stems, which should be reflective of row spacing, there was no relationship
between row-spacing treatment and this component (Owens 2011). Stand age was
important to birds through its effect on vegetation structure, a pattern consistent with
other studies (DeGraaf 1991, Keller et al. 2003, Lane 2010, Lane et al. 2011).
Successional change appeared to be particularly important for facilitating breeding
because breeding scores, a measure of overall breeding activity on the plots,
Table 4. Model-selection results comparing analyses of covariance (ANCOVA), which test response
of avian species richness, abundance, and breeding activity to debris placement and 3 vegetation
characteristics. L represents likelihood. S, E, and G represent composite variables vegetation structure,
evergreen cover, and groundcover, respectively (see Table 3). D refers to debris placement, and
* indicates an interaction between two variables. Only competitive (ΔAICC < 4), global, and null
models are shown (see Owens [2011] for results of all models). The global model is defined as D S
E G D*S D*E S*E.
Response variable/model AICC ΔAICC L Weight K -2logL
Species richness
SE 47.89 0.0 1.000 0.365 3 34.14
SEG 49.07 1.2 0.549 0.200 4 32.69
SES*E 49.35 1.5 0.472 0.172 4 32.97
DSE 50.38 2.5 0.287 0.105 5 33.99
SEG S*E 50.92 3.0 0.223 0.081 5 31.79
DSEG 51.46 3.6 0.165 0.060 6 32.32
GLOBAL 57.15 9.3 0.010 0.004 11 29.01
NULL 67.97 20.1 0.000 0.000 1 59.17
Abundance
SEGS*E 147.46 0.0 1.000 0.912 5 128.39
GLOBAL 154.25 6.8 0.033 0.030 11 126.25
NULL 198.60 51.1 0.000 0.000 1 189.82
Breeding score
SEG 431.97 0 1.000 0.442 4 415.63
SEGS*E 432.48 0.5 0.779 0.344 5 413.42
D S E G 434.54 2.6 0.273 0.121 6 415.48
GLOBAL 440.47 8.5 0.014 0.006 11 412.47
NULL 506.72 74.8 0.000 0.000 1 500.26
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steadily increased over the years of the study, ultimately reaching about 15 times
the activity in 2010 as in 2006 (Fig. 5). Species richness of potential breeders increased
more modestly, approximately doubling in the same interval.
Species composition during the first 5 years after stand establishment was
characterized more by addition of species than by replacements. This trend has
been observed previously in young, regenerating forest stands, where loss of earlysuccessional
bird species did not occur until canopy closure, sometime between the
5th and 10th year of growth (Keller et al. 2003, LeGrand et al. 2007). By the end of
our study, we saw increased abundance of some closed-canopy forest species, such
as Carolina Chickadee and Tufted Titmouse, and a decline in some early-successional
species, such as Eastern Bluebird (Sialia sialis [L.])] and Eastern Kingbird
(Tyrannus tyrannus [L.]), probably indicating the beginning of a shift toward
closed-canopy forest species as canopy and understory developed. By 7–10 years
after planting, we would expect stands with narrow row-spacing to support closedcanopy
species such as Limnothlypis swainsonii (Audubon) (Swainson’s Warbler;
Bassett-Touchell and Stouffer 2006).
Our study focused on community-level trends, but it is important to note that the
efficacy of grouping entire communities of species, especially when making management
recommendations, has its limitations. Individual species belonging to these
communities differ in preferred foraging substrate, preferred nesting substrate, and
level of specialization—variation that can go undetected when community-level
metrics are employed. Land managers require information that enables them to
provide habitat conditions for multiple species, but when particular species are
of interest, species-level studies help tailor management strategies to their particular
habitat needs. Our findings suggest that managing for early-successional
species may effectively provide habitat for some species of conservation concern.
Table 5. Analysis of covariance results for models that best explain the response of bird community
measures to debris placement and vegetation characteristics, as determined via model selection. Tests
on species richness and abundance used square root transformed data.
Response variable/predictor variable Estimate SE df t P
Species richness
Intercept 7.56 0.006 3.8 35.71 less than 0.001
Vegetation structure 0.05 0.004 39.7 3.52 0.001
Evergreen cover 0.03 0.003 50.3 3.69 less than 0.001
Abundance
Intercept 32.89 0.124 3.9 16.31 less than 0.001
Vegetation structure 0.86 0.018 42.5 6.90 less than 0.001
Evergreen cover 0.48 0.012 47.6 6.25 less than 0.001
Vegetation structure*Evergreen Cover 0.36 0.035 52.0 -3.19 0.002
Groundcover 0.36 0.034 54.0 3.27 0.002
Breeding Score
Intercept 29.96 2.008 4.1 14.92 less than 0.001
Vegetation structure 13.01 1.960 47.7 6.64 less than 0.001
Evergreen Cover 16.22 1.563 53.7 10.38 less than 0.001
Groundcover 6.49 2.471 52.8 2.63 0.011
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However, this does not necessarily reduce importance of attending to variation on
the species or guild level.
Because the row spacing and debris treatments we studied did not affect birds,
we recommend that managers of intensively managed pine forests in the southeastern
US implement the combination of row spacing and debris placement
that best meets timber management objectives. However, the wider row spacing
examined in this study may be favorable for disturbance-dependent birds in
other settings. For example, Lane et al. (2011) found that birds benefited from
6.1-m row spacing (one of the widths in our study) versus 3.0-m rows (a spacing
common in industrial plantations, but narrower than those tested in our study) in
North Carolina Loblolly Pine plantations, although these results were confounded
with CWD management. Additionally, wider rows increase the time until pinecanopy
closure occurs, potentially extending the longevity of beneficial habitat
conditions for early-successional bird species. Because disturbance-dependent
birds responded to general structure, evergreen cover, and groundcover, site
preparation and stand establishment methods that positively influence these vegetation
characteristics may prove more beneficial to birds than row spacing per
se. Timberland managers already work toward maximizing growth of target tree
species, concurrently speeding development of structure and cover for birds. Similarly,
past research has indicated that increased herbaceous groundcover is often
a consequence of vegetation control during stand establishment (e.g., Chamberlain
and Miller 2006, Jones et al. 2009a, Miller et al. 2009). Once plantations
develop woody structure between planted pine rows, selective herbicides used
to control hardwoods (e.g., imazapyr) can extend the window of time that herbaceous
vegetation dominates the site (e.g., Welch et al. 2004).
We note that this study did not encompass the entire early-successional phase
on our experimental plots. As species turnover in the bird community occurs over
time and forest specialists begin to replace early-successional species, community
responses to row spacing and debris arrangements may appear. For example as mentioned
above, wider row spacing may affect timing of canopy closure, extending the
early-successional phase of stands. Clearly, there are research needs and opportunities
regarding early-successional bird use of intensively managed pine plantations.
Acknowledgments
Thanks go to M.D. Kaller for providing statistical advice. We acknowledge the work
of A.T. Salisbury (LSU) and C. Reynolds (Weyerhaeuser) during preliminary years of the
study. We thank J. Nehlig at Lee Memorial Forest, Franklinton, LA, and J. Johnson at
Jackson Bienville Wildlife Management Area, Quitman, LA, for offering lodging during
data collection. L.M. Palasz, M.E. Brooks, K.S. Mokross, L.L. Powell, C.M. Leumas, E.E.
DeLeon, J.D. Wolfe, J.L. Grace, I.T. Knowles, and J.A. Trienekens provided essential help
in the field. Paul Leberg and two anonymous reviewers made helpful suggestions that improved
the manuscript. Funding was provided by Weyerhaeuser Company. This manuscript
was approved for publication by the Director of the Louisiana Agricultural Experimental
Station as manuscript number 2014-241-15529.
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2014 Vol. 13, No. 3
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Appendix A. Mean abundance of bird species detected in study plots between late-April and mid-July in 2006, 2007, 2009, and 2010. Species
are arranged in order of decreasing total detections. * indicates disturbance-dependent species; † signifies species breeding on plots,
and ‡ denotes species of conservation concern. Piled and scattered refer to debris, and 4.3 and 6.1 refer to row spacing (m). Conservation
score refers to regional combined score for the breeding season (RCS-b), as defined by Pranjabi et al. (2005). Full species names can be
found at http://www.birdpop.org/alphacodes.htm. Total = total detected, yrs = years detected.
Conservation
Mean abundance
score for 2006 2007 2009 2010
sites Piled Scattered Piled Scattered Piled Scattered Piled Scattered
Species A+B C+D 4.3 6.1 4.3 6.1 % 4.3 6.1 4.3 6.1 % 4.3 6.1 4.3 6.1 % 4.3 6.1 4.3 6.1 % Total Yrs
YBCH*† 13 13 1 1 2 4 7 9 12 8 22 29 28 33 16 28 27 24 36 18 261 4
INBU*† 14 11 3 3 4 6 12 10 17 13 14 14 17 26 22 23 12 23 25 18 21 14 245 4
PRAW*†‡ 18 18 0 5 5 6 5 5 18 20 17 19 11 13 12 18 13 9 151 3
BLGR*† 12 12 3 3 6 8 9 9 9 8 9 13 11 10 10 6 8 11 7 8 5 125 4
CARW† 13 13 3 4 4 4 11 7 8 8 10 9 5 8 4 8 4 4 8 6 6 4 97 4
NOCA† 12 10 4 4 5 2 11 8 2 7 7 6 7 6 6 6 4 7 6 8 7 4 92 4
EATO*† 16 10 1 1 2 7 2 3 4 4 8 11 9 10 5 10 6 10 9 6 91 4
COYE*† 13 13 0 3 8 5 5 5 7 8 8 9 5 6 5 6 7 4 77 3
OROR†‡ 16 18 0 3 5 6 1 4 9 7 11 7 5 9 7 5 6 4 76 3
EAKI† 15 13 1 1 3 4 6 7 5 10 4 5 5 3 3 4 1 3 2 56 4
NOMO† 12 10 1 3 3 2 2 3 4 3 5 6 6 4 3 5 4 7 3 3 55 4
CACH 16 16 2 3 5 8 2 2 1 4 4 3 7 2 6 7 3 6 4 54 4
BHCO† 8 11 0 4 1 1 8 3 5 5 3 7 8 4 8 4 53 3
WEVI*† 14 16 1 1 2 1 1 2 2 1 5 2 4 4 2 5 5 9 5 4 47 4
MODO 11 8 1 1 4 1 5 4 3 2 3 3 1 4 1 3 1 3 1 1 1 1 34 4
BGGN† 11 13 1 1 1 1 1 5 4 4 4 2 3 4 2 2 2 31 4
NOBO*† 16 15 0 1 1 1 1 4 5 4 3 2 2 4 2 2 2 29 3
EABL* 11 11 3 1 1 1 5 3 4 1 2 3 2 4 3 2 0 26 3
FISP*† 15 15 0 1 0 2 3 3 1 1 1 2 6 3 2 22 3
BRTH* 15 13 0 1 1 1 1 2 1 1 4 1 1 2 3 3 1 20 3
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Conservation
Mean abundance
score for 2006 2007 2009 2010
sites Piles Scattered Piles Scattered Piles Scattered Piles Scattered
Species A+B C+D 4.3 6.1 4.3 6.1 % 4.3 6.1 4.3 6.1 % 4.3 6.1 4.3 6.1 % 4.3 6.1 4.3 6.1 % Total Yrs
BLJA 14 13 1 2 1 2 5 1 0 3 0 2 1 1 1 14 4
TUTI 13 14 3 2 0 3 1 4 1 1 2 0 14 3
SEWR‡ - - 0 2 2 2 3 2 0 4 1 1 14 2
RHWO*†‡ 15 17 2 1 4 1 1 2 3 2 1 0 14 3
GCFL 12 13 1 1 2 2 1 1 2 1 1 1 1 1 1 0 12 4
RTHU 12 13 1 1 2 1 1 1 1 2 2 1 1 1 1 0 12 4
RBWO 13 12 3 2 1 0 1 4 1 2 0 11 4
PIWA 14 14 2 2 0 2 1 0 4 2 1 11 3
SUTA 16 15 1 1 1 2 1 1 2 2 1 1 1 0 11 4
SWSP - - 0 2 3 2 2 2 0 1 0 10 2
COGR 11 11 0 2 2 3 2 2 0 1 0 10 2
GRCA 11 9 0 1 0 2 2 2 1 1 1 0 9 3
HOWA 14 16 2 2 0 3 1 1 1 1 0 8 3
CGDO*‡ 16 11 0 1 1 1 2 0 2 0 6 3
NOFL 15 14 0 1 0 4 1 1 0 6 2
REVI 11 11 3 1 2 4 0 0 0 6 1
AMCR 11 10 1 1 0 1 1 1 0 0 4 2
BHNU‡ 20 19 2 2 0 1 0 1 0 4 3
CHSP 9 11 1 1 2 2 1 0 0 4 2
HOWR 8 8 0 2 1 1 1 0 0 4 2
RWBL 11 10 0 1 0 1 0 2 0 4 3
DOWO 14 13 0 0 1 0 2 0 3 2
RTHA 9 9 0 2 1 1 0 0 3 1
SOSP 8 - 2 2 1 0 0 0 3 2
YTVI 15 15 1 1 0 1 0 1 0 3 3
WTSP - - 0 1 1 1 0 0 2 1
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Conservation
Mean abundance
score for 2006 2007 2009 2010
sites Piles Scattered Piles Scattered Piles Scattered Piles Scattered
Species A+B C+D 4.3 6.1 4.3 6.1 % 4.3 6.1 4.3 6.1 % 4.3 6.1 4.3 6.1 % 4.3 6.1 4.3 6.1 % Total Yrs
AMKE 13 13 0 1 0 0 0 1 1
AMRO 6 9 1 1 0 0 0 1 1
BACS*‡ 21 20 0 1 0 0 0 1 1
CWWI 16 16 0 0 1 0 0 1 1
EAWP 14 16 1 1 0 0 0 1 1
MAWR 14 - 0 0 0 1 0 1 1
TUVU 9 10 1 1 0 0 0 1 1
WITU 11 11 0 0 1 0 0 1 1
YBCU† 15 15 0 0 0 1 0 1 1
YRWA - - 0 1 0 0 0 1 1
Total 34 22 39 34 100 83 94 108 106 100 172 190 166 181 100 159 158 143 164 100 1853