Patuxent Wildlife Research Center
Models for Managing Habitat of a
Swainson’s Warbler Breeding Population, Bond Swamp National Wildlife
The Swainson’s Warbler (Limnothlypis swainsonii) is a neotropical migratory songbird which breeds predominantly in bottomland hardwood forests of the southeastern United States (Meanley 1971, Brown and Dickson 1994). Its population status and trends are unclear (Brown and Dickson 1994, Sauer et al. 2001). It appears, however, that populations have declined throughout the species’s breeding range as the result of agricultural, forestry, and watershed management practices. These practices may have substantially altered southeastern floodplains dense thickets of a native bamboo (Arundinaria gigantea) or cane, which may be important habitat of breeding territories in some areas (Meanley 1971, Brown and Dickson 1994). Little quantitative information is available about the characteristics of habitats used by Swainson’s Warblers in core areas of the breeding range (Meanley 1945, 1966, 1971). In Virginia, Graves (2001) created a 5-variable habitat model which correctly classified 90% of the 74 habitat plots used to create the model in terms of presence versus absence of Swainson’s Warblers. Interestingly, cane stem density was negatively correlated with Swainson’s Warbler presence in the Virginia data set, and median number of cane culms was about five times greater in random plots than territory plots. Total number of trees with diameter breast height (dbh) <5 cm and greenbrier (Smilax sp.) stem density were the variables most positively correlated with Swainson’s Warbler presence. Standing or pooled water was recorded in 61% of random plots, but not in any territory plots, and presence of standing water was positively correlated with cane stem density at the Virginia site. The latter result seems to contradict information assembled by Platt and Brantley (1997), which suggests that cane may benefit from seasonal flooding (perhaps due to nutrient transport into cane patches), but may be killed by prolonged inundation and thus prefers relatively dry bottomland soils.
This research was made possible only with funding and support from our partners: U.S. Fish and Wildlife Service - Region 4, Warnell School of Forest Resources Alumni Scholarship Fund, and the Georgia Ornithological Society.
Our study area was located within Bond Swamp National Wildlife
Refuge (BSNWR) administered by the U.S. Fish and Wildlife Service (USFWS).
BSNWR comprises 2,630 ha of land located along the upper Ocmulgee
River (pictured below) in Bibb and Twiggs counties, Georgia, ca. 10 km
south of the city of Macon. Habitat
types include bottomland hardwood forest, tupelo gum (Nyssa
aquatica) swamp forest, and mixed pine/hardwood ridges, along
with beaver swamps, oxbow lakes, and tributary creeks.
Some areas of the bottomland hardwood forest have characteristics
of old growth forests (e.g., very large trees and openings).
The refuge is typically inundated by seasonal floodwaters in late
winter through spring, and some swamp forest areas remain inundated
year-round (USFWS 2000). We
focused our research within the main body of BSNWR, in an area of ca.
Field data collection—We established 21 transects totaling 18.4
km in April-June 2001 for Swainson’s Warblers searches at BSNWR .
Swainson’s Warbler territories were located also by searching the
entire refuge in addition to conducting distance sampling and playback
surveys for territorial Swainson’s Warblers along the transects.
From late April through mid-June 2001, we collected data by
following singing males as they moved through their territories,
maintaining a distance of at least 10 m from these individuals.
During our intensive territory observations, we recorded GPS
coordinates for each location at which the individual bird was seen or
heard. Each individual was
followed for at least 1-2 hr. We
obtained an average of 20 locations per individual.
We produced a simple map (10 x 10 m grid) from GPS locations of
each SWWA territory. These
maps identified a 50 x 50 m (0.25 ha) area used most intensively by each
individual during our territory observations.
We then established the 50 x 50 m plot above for measurement of
habitat variables in the corresponding area of each territory.
In addition, we established 50 x 50 m (0.25 ha) random unoccupied plots for habitat measurements along our transects for the Swainson’s Warbler surveys. We measured the following habitat variables in all Swainson’s Warbler territory plots and random plots between 20 June and 1 August 2001: average cane height (m) in plot, to nearest 0.25 m, based on mean of 2 observers; total cane area (m2) in plot, mean from 2 observers; number of cane stems on 2 perpendicular 3.5- x 50-m belt transects (stem density); number of shrub, vine, and small tree stems (≤2.5 cm dbh) on same transects above (stem density); number of samples with dead ground cover (100 point samples of dead ground cover, every m on transects); number of samples with no dead ground cover (ibid.); number of samples with water ground cover (ibid.); number of samples with broad-leaved forb ground cover (100 point samples for live ground cover, every m on transects); number of samples with grass ground cover (ibid.); mean leaf litter depth from 20 samples, taken every 5 m along transects; % canopy cover (number of point samples with canopy cover present, out of 100 samples; diameter at breast height (dbh in cm) of tree in plot with maximum dbh; distance from center of plot (m) largest tree (dbh); number of tree falls in plot; number of canopy tree openings in plot; basal area (x 10 = sq. ft./acre); pH water of plot soil sample; pH salt (CaCl2) of plot soil sample; % silt in plot soil sample; % sand in plot soil sample; % clay in plot soil sample; and electrical conductivity (deciSiemens/m [dS/m]) of plot soil sample.
We used SAS Version 8 for statistical analysis of habitat plot data. We analyzed data for 56 Swainson’s Warbler territory plots (SWWA) (50 x 50 m, n = 56) and 110 randomly selected and unoccupied (RAND) plots. We developed a set of alternative multivariate habitat models for Swainson’s Warbler territories at BSNWR using stepwise binary logistic regression. We avoided potential multi-collinearity by preparing a Pearson correlation matrix prior to logistic regression analysis and eliminated 4 variables that were highly correlated (r2 > 0.49) with others in our data set. We then ran several different logistic regression analyses with the remaining 18 variables, using varying alpha-levels for entry and retention of variables in each model (alpha = 0.05, 0.10, and 0.15, respectively).
We developed 3
habitat models using logistic regression, with increasingly permissive
alpha-levels for entry and retention of variables in each model.
The most parsimonious model included 4 variables: cane stem
density, shrub+vine+sapling stem density, mean litter depth, and ground
coverage by water (alpha = 0.05 for both entry and retention; AIC = 80.6).
Other models contained more variables (6-11) and described the
habitat in more detail, both biologically and intuitively.
Not surprisingly, predictive capability improved with the addition
of more variables to the model (i.e., with increasingly "-levels), as
estimated by several rank correlation indices calculated for the three
models (SAS Institute Inc. 1990). Rank
correlations for assessing predictive ability of the models by Goodman-Kruskal
Gamma values were 0.941, 0.950, and 0.981 (out of 1.00) for the 4-, 6-,
and 11-variable models, respectively.
Percent concordance of the predicted probabilities and observed
responses were high for all models (97.0, 97.5, and 99.0 respectively).
analysis confirms that cane is an important component of Swainson’s
Warbler breeding habitat at BSNWR. Not
only were mean cane area, cane height, and cane stem density much greater
in territories than in random plots, but cane stem density was the most
significant variable entered into each of our 3 logistic regression
models. The greater
shrub stem density we found in territories, compared with unoccupied
random plots, is of interest with regard to Graves’s (2001) work in
Virginia. Shrub stem density
was the second variable entered in our logistic regression models,
demonstrating that density of non-cane shrub-level vegetation may be
nearly as important as the cane-related variables in separating
Swainson’s Warbler territory plots from unoccupied plots at BSNWR.
While Swainson’s Warblers may preferentially establish breeding
territories within cane patches in areas where cane is abundant, overall
structure of the shrub layer (including both cane, if present, and other
shrub-layer species) may be a more important factor in determining
suitable breeding habitat. The
shrub stem count in our study area also included vines such as greenbriar.
In Graves’s (2001) logistic regression model, Greenbriar
abundance was one of the most important variables for predicting
Swainson’s Warbler presence.
Leaf litter depth was the third most important variable in our
models and was greater in territories than in random plots.
Conversely, unoccupied plots had a greater percentage of areas with
no leaf litter and/or the presence of standing water; the latter variable
was the fourth most important variable in the habitat models, and was
negatively correlated with Swainson’s Warbler territories. As in our
study, Graves (2001) also found the presence of standing water to be
negatively correlated with Swainson’s Warbler presence in Virginia.
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Patuxent Wildlife Research Center, Laurel, MD, USA 20708-4038
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Last modified: 01/08/2003
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