2008 NORTHEASTERN NATURALIST 15(4):607–618
Decline of a New Hampshire Bicknell’s Thrush Population,
1993–2003
J. Daniel Lambert1,2,*, David I. King3, John P. Buonaccorsi4,
and Leighlan S. Prout5
Abstract - Catharus bicknelli (Bicknell’s Thrush) is a rare inhabitant of mountain
forests in the northeastern United States and southeastern Canada. Conservation
planners consider the species to be at risk, although evidence of population decline
has thus far been localized or inconclusive. In order to assess the status of Bicknell’s
Thrush in the White Mountains of New Hampshire, we conducted point-count surveys
on 40 forested, high-elevation routes from 1993 to 2003. Non-linear regression on
aggregate counts revealed a 7% annual decline over this period (P < 0.1). We discuss
possible threats to Bicknell’s Thrush, including winter habitat loss, pollution of mountain
ecosystems, climate change, and human intrusion during breeding. A range-wide
monitoring program that incorporates new survey methods is needed to help identify
limiting factors and reduce potential sources of error and bias. This study underscores
the importance of efforts to monitor and conserve Bicknell’s Thrush.
Introduction
Catharus bicknelli (Ridgway) (Bicknell’s Thrush) is a rare, forest-dwelling
passerine that nests at upper elevations in New York, Vermont, New Hampshire,
and Maine (Atwood et al. 1996, Lambert et al. 2005), and in scattered highland
and coastal areas of southeastern Canada (Nixon 1999, Ouellet 1993). Its
global population may number fewer than 50,000 individuals; however, habitat
and density data are too scarce to permit an accurate estimate (Rimmer et al.
2001, 2005a). A Bicknell’s Thrush abundance model for New Hampshire’s
White Mountains, derived from point counts, habitat measurements, and satellite
imagery, produced an estimate of 5000 birds (95% CI = 900–23,000; Hale
2006). The White Mountains lie at the core of the species’ breeding range and
contain one-third of the potential Bicknell’s Thrush habitat in the United States
(Lambert et al. 2005; J.D. Lambert, unpubl. data).
Bicknell’s Thrush primarily inhabits young or chronically disturbed forests
dominated by Abies balsamea (L.) P. Mill. (Balsam Fir), with variable amounts
of Picea rubens Sarg. (Red Spruce) and Betula papyrifera var. cordifolia
(Regel) Fern. (Paper Birch) (Atwood et al. 1996, Connolly et al. 2002). It also
utilizes regenerating stands of mixed forest in northern New England (Vermont
Center for Ecostudies [VCE], Norwich, VT, unpubl. data), Québec (Ouellet
1Vermont Institute of Natural Science, PO Box 1281, Quechee, VT 05059. 2Current
address - American Bird Conservancy, c/o Vermont Center for Ecostudies, PO Box
420, Norwich, VT 05055. 3US Forest Service Northern Experiment Station, 203
Holdsworth Natural Resources Center, University of Massachusetts, Amherst, MA
01003. 4Department of Mathematics and Statistics, University of Massachusetts,
Amherst, MA 01003. and 5White Mountain National Forest, 719 North Main Street,
Laconia, NH 03246. *Corresponding author - dlambert@abcbirds.org.
608 Northeastern Naturalist Vol. 15, No. 4
1993), and Nova Scotia (Campbell and Whittam 2006). This alternative habitat,
which follows disturbance by fire or logging, contains the majority of New
Brunswick’s breeding population (Nixon et al. 2001). Most typical and alternative
habitats are characterized by high vegetation density and canopies less
than 6 m in height (Connolly et al. 2002, Nixon et al. 2001, Rimmer et al. 2001,
Sabo 1980). Wintering birds concentrate in montane broadleaf forests of the
Dominican Republic (Rimmer et al. 2001), with isolated populations in Haiti
(Rimmer et al. 2005b) and eastern Cuba (Rompré et al. 2000).
Its limited distribution, low numbers, and specialized habitat requirements
make Bicknell’s Thrush vulnerable to extinction. New York, Vermont,
New Hampshire, Maine, and Nova Scotia all list the songbird as a species of
special concern, as does the Committee on the Status of Endangered Wildlife
in Canada (COSEWIC 1999). It is extirpated from Massachusetts (Veit and
Petersen 1993). The North American Bird Conservation Initiative identifies
Bicknell’s Thrush among the highest priority landbirds in the Atlantic Northern
Forest (Dettmers 2003), while Partners in Flight has placed the species on
its continental watch list, citing multiple causes for concern (Rich et al. 2004).
Bicknell’s Thrush has disappeared from several island and coastal locations
in Canada during the 20th century (Nixon 1999), and from low-mountain sites
in the United States (Atwood et al. 1996, Lambert et al. 2001). Although these
extirpations suggest a decrease in overall numbers, there has been no largescale
assessment of population trends for this species. The North American
Breeding Bird Survey (BBS), which is conducted along roads, has recorded
just 74 Bicknell’s Thrushes since 1966, nearly half of these in Nova Scotia
(USGS Patuxent Wildlife Research Center 2006). Two routes that pass
through mountain notches account for most of the US observations. To address
the gap in coverage, in 1993 the US Forest Service initiated standardized surveys
of mountain birds in the White Mountain National Forest. In this paper,
we present trend analyses for the first decade of monitoring (1993–2003) to
provide information on the status of Bicknell’s Thrush populations in montane
spruce-fir forests in the White Mountains.
Methods
Field-site description
Forty high-elevation routes were established systematically along footpaths
in the White Mountain National Forest, located in north-central New
Hampshire. Routes were selected to provide broad geographic coverage
and a representative sample of montane spruce-fir forest (Fig. 1). A typical
route started at the lower spruce-fir ecotone, continued up the path through a
variety of age classes, and over the mountain until it passed back out of the
focal habitat. Sampled elevations ranged from 740 m to 1470 m, with some
stations falling on exposed ridgelines or in patches of mixed forest adjacent
to conifer-dominated stands.
Field survey
From 1993 to 2003, trained observers conducted annual point counts
between 5 and 28 June. Observers performed their first count at a fixed point
2008 J.D. Lambert, D.I. King, J.P. Buonaccorsi, and L.S. Prout 609
and used a rope to measure 250-m intervals between each of the subsequent
points. We reviewed field records to verify that points were consistently
placed and eliminated counts if evidence indicated an error in point placement.
Surveys were completed between 0500 and 1100 EST. Observer
turnover was high, with 30 observers participating for one year, 7 participating
for two years, and another 2 observers participating for three and four
years, respectively. For each route, start times were standardized and count
dates fell within the same 10-day window each year. Observers recorded all
individuals seen and heard during silent, 5-min counts. Surveys were not
conducted in the rain or when wind speeds exceeded 40 km/h. To assess
the effect of wind on our trend estimates, we ran analyses with and without
counts conducted in winds of 31 to 40 km/h.
Figure 1. Location of routes surveyed for Bicknell’s Thrush population trend analysis,
1993–2003. Shaded areas represent potential Bicknell’s Thrush habitat within
and bordering the White Mountain National Forest (Lambert et al. 2005). Squares
depict routes with pronounced decline (trend + 2 SE < 1.0).
610 Northeastern Naturalist Vol. 15, No. 4
Analysis
Our primary analysis estimates an overall trend by fitting total yearly
counts, aggregated over routes, as a function of time. This approach calls for
a common set of routes, and points within routes, to be surveyed for each
year. Originally, our data did not meet this requirement for consistency because
several surveys were canceled or cut short due to rain or high winds.
To resolve this issue, we used two subsets of data, the first providing maximum
temporal coverage (17 routes in 9 years) and the second providing
larger geographic coverage (39 routes in 7 years). The effective route length
averaged 10.6 points (± 4.2 SD) in the first group (range = 1–18) and 11.8
points (± 3.2 SD) in the second group (range = 4–18).
Once a common set of routes and points was designated, we assumed the
count in year y (Cy) followed the model, E(Cy) = a * by, where the trend coeffi-
cient b is the annual rate of change in the population size. We fit this model using
weighted non-linear least squares (via PROC NLIN in SAS), assuming the variance
of the count was proportional to the expected count. This method is simply
Poisson regression, allowing for over- or under-dispersion, and is equivalent
to using the estimating equation approach of Link and Sauer (1994). The estimated
trend applies to the total area defined by the collection of routes entered
into the analysis. This analysis also produces standard errors and confidence intervals
for both the coefficients in the model and for the estimated abundances
at given points in time. We investigated the adequacy of the model both through
residual analyses and by fitting a nonparametric curve (using LOESS in SAS),
and our model appeared to fit the data well (see Fig. 2).
Figure 2. Fitted
curve (solid),
LOESS fit
(dashed), and
95% confidence
interval (dotted)
for Bicknell’s
Thrush population
trends in the
White Mountains
on (A) 17 routes
surveyed in 9
years (1993–2000,
2003) and (B) 39
routes surveyed
in 7 years (1993,
1995–2000).
2008 J.D. Lambert, D.I. King, J.P. Buonaccorsi, and L.S. Prout 611
Since the data come from a time series, we also examined the residuals
to check for serial correlation in the error term. Plots of the residual
versus time and versus the previous residual showed that serial correlation
was relatively weak in both analyses. Numerically, the correlation between
adjacent residuals was 0.11 and 0.20 for the analyses with nine and seven
years, respectively. For these reasons, we have used the non-linear regression
analysis, assuming uncorrelated errors.
We carefully considered whether to include route or observer effects
in our models, and determined that their inclusion was not warranted.
Although route characteristics influence bird counts, our treatment of
the routes as fixed essentially builds these effects into the model. Thus,
route effects do not need to be explicitly modeled in order to estimate the
overall trend. The commonly employed route-regression technique, which
estimates overall trend with a weighted average of route-specific trends
(e.g., Geissler and Sauer 1990), could not be applied as it requires routes
to be placed randomly. Even with random routes, there are additional
questions as to how the weighted average of individual trends estimates a
regional trend (although a mixed-model framework has begun to address
this issue; see Link and Sauer 2002). However, information on routespecific
trends can be useful for examining spatial patterns in population
change. We therefore fit a trend for each route, using a common set of
points sampled in each year. The number of years of data varied among
routes, depending on sampling history. In some cases, the model fit was
poor due to low counts, as indicated by the large confidence intervals on
estimated trends (see Fig. 3).
We were not able to include observer effects in our analyses because
observer turnover was very high (95% of the observers participated in just
one or two years), making it impossible to separate observer effects from
year effects.
Figure 3. Routelevel
estimates
of Bicknell’s
Thrush population
trends
in the White
Mountains (±
2 SE). Route
numbers on xaxis
correspond
with locations
mapped in Figure
1.
612 Northeastern Naturalist Vol. 15, No. 4
Results
We detected 280 Bicknell’s Thrushes on the 17 routes that were sampled
in each of the nine survey years (1993–2000, 2003). We recorded 624 individuals
on the 39 routes that were surveyed in seven of the years (1993,
1995–2000). The non-linear regression on aggregate counts revealed negative
trends for the subset of data that incorporated the most years (trend ±
SE = 0.93 ± 03, 95% CI = 0.85–1.01, P = 0.06) and for the subset that incorporated
the most routes (trend ± SE = 0.93 ± 03, 95% CI = 0.85–1.01, P
= 0.09) (Fig. 2). These findings correspond with a rate of decline averaging
7% per year. Both data series were characterized by an abrupt decrease in
the total count between 1997 and 1998. On the 17 routes surveyed in 9 years,
Bicknell’s Thrush numbers were 64% lower in 2003 (total = 47) than in 1997
(total = 17). On the 39 routes monitored in 7 years, the count was halved
between 1997 (total = 116) and 2000 (total = 58).
Discarding counts that were conducted during periods of elevated wind
(31–40 km/h) led to the elimination of certain routes in certain years and
therefore limited which routes could be used in the aggregate analysis. In
fact, a curve could not be fit using all years. We were able to fit a curve to
data from 1993 and 1995–2000, after the windy counts were eliminated.
The resulting trend estimate (0.95) was similar to the original value (0.93);
however, it lacked statistical significance (P = 0.5). We note that the trend of
0.95 was estimated from only 12 routes, compared to the 39 routes included
in the analysis of counts made under all wind conditions less than 40 km/h in 1993
and 1995–2000.
Route-specific trends, estimated for 40 White Mountain routes (Fig. 3),
were negative on 29 routes and positive on 11 routes. The most pronounced
population changes were declines, which were detected throughout the study
area (Fig. 1) and across a wide range of elevations. The two greatest declines occurred
in small habitat patches on Terrace Mountain (route 39; trend = 0.60) and
Mount Crawford (route 26; trend = 0.66). Routes located in extensive habitat
of the Presidential Range (routes 28–32) and the Carter-Moriah Range (routes
34–36) showed the least evidence of population decline (Figs. 1 and 3).
Discussion
The decline of Bicknell’s Thrush in our survey area is consistent with a
pattern of local extirpations. During the 20th century, Bicknell’s Thrushes
have disappeared from several island and coastal locations in Canada
(Nixon 1999) and from low-mountain sites in the United States (Atwood et
al. 1996, Lambert et al. 2001). A peripheral population on Mount Greylock,
MA fell from an estimate of 10 pairs in the 1950s to 0 in 1973 (Veit and
Petersen 1993). Data from the BBS indicate that Bicknell’s Thrushes have
also disappeared from Dixville Notch, NH and Black Brook Notch, ME
after occupying these sites for a number of years (USGS Patuxent Wildlife
Research Center 2006). Road-based BBS counts have produced few other
records of this mountain-dwelling species in the United States. Even when
US and Canadian BBS records are combined, observations of Bicknell’s
2008 J.D. Lambert, D.I. King, J.P. Buonaccorsi, and L.S. Prout 613
Thrush are too sparse to meet minimum requirements for trend estimation.
While local extirpations may be symptomatic of a declining population, they
also occur in stable populations as a result of changing habitat conditions. It
is especially difficult to generalize site-based information for species, like
Bicknell’s Thrush, that utilize ephemeral habitat patches within a forest type
that is subject to disturbance and re-growth.
Surveys at migration stopover sites corroborate the evidence of decline in
the breeding areas. Wilson and Watts (1997) reported a decrease in autumn
capture rates for Bicknell’s Thrush and Catharus minimus Lafresnaye (Graycheeked
Thrush) between 1968 and 1995 in coastal Virginia. The two species
were analyzed together because C. bicknelli was considered a subspecies of
C. minimus until 1995 and because morphometric overlap is considerable.
The authors’ analytical approach, rank correlation, detected a significant reduction
in captures over time, but yielded no estimate of trend. Capture rates
of Gray-cheeked/Bicknell’s Thrushes also declined in coastal Massachusetts
between early years (1970–1985) and late years (1986–2001), dropping 66%
and 44% during spring and fall migrations, respectively (Lloyd-Evans and
Atwood 2004).
Mountain Birdwatch, an effort to monitor high-elevation breeding birds
at sites in New York, Vermont, New Hampshire, and Maine, measured a 9%
annual decline (P = 0.07) in the relative abundance of Bicknell’s Thrush between
2001 and 2004 (n = 47 routes; Lambert 2005). This trend was similar
to what we observed in the White Mountain population between 1993 and
2003, though the two studies had only one year in common (2003). In adjacent
areas of Canada, the High Elevation Landbird Program detected significant
reductions in Bicknell’s Thrush numbers between 2003 and 2007 (Campbell
et al. 2007; B. Whittam, Bird Studies Canada, Sackville, NB, Canada, pers.
comm.). Forestry operations in the New Brunswick survey area (e.g., precommercial
thinning) may have influenced results from that province.
If negative trends persist in the White Mountains, the region’s estimated
population of 5000 birds (Hale 2006) could be endangered within a few
decades. Based on the historic pattern of extirpation (Nixon 1999, Veit and
Petersen 1993), low mountains with small habitat patches would be at greatest
risk of losing the species. Mount Crawford (951 m) and Terrace Mountain
(1114 m), sites of the most abrupt declines, match this profile. In contrast,
clusters of stable or increasing trends were observed in large habitat blocks,
including the vast montane spruce-fir of the Presidential Range.
Short-term changes in Bicknell’s Thrush abundance may not accurately
reflect long-term trends, as bird populations may fluctuate over time (King
et al. 2006, Peterjohn et al. 1995). The trends we report might even reverse
themselves. Mountain Birdwatch results from 2005–2007 support this possibility,
showing an increase in Bicknell’s Thrush numbers on New York and
northern New England survey routes (VCE, unpubl. data) following a period
of decline over the four previous years (Lambert 2005).
It is also possible that the population decrease observed in this study was
offset by gains elsewhere in the White Mountains. However, we believe that
this is unlikely for three reasons. First, none of the widely distributed routes
614 Northeastern Naturalist Vol. 15, No. 4
detected a significant increase during the survey interval. Second, most of
the population change occurred during a three- to six-year period, a span too
short for significant shifts in the location of suitable habitat. Finally, Bicknell’s
Thrush exhibits high site fidelity and can live to be 10 or more years
under favorable conditions (VCE, unpubl. data).
Factors underlying the observed decline are unknown, but potential
threats to Bicknell’s Thrush include: winter habitat loss, atmospheric pollution,
climate change, and disturbance by hikers. The Dominican Republic,
where most Bicknell’s Thrushes overwinter, has lost approximately 90% of
its native forest (Stattersfield et al. 1998), including large areas of montane
broadleaf habitat. Although winter habitat loss has been extensive, evidence
of winter limitation is lacking. Studies are needed to quantify the extent
and use of remaining winter habitat. Changes to breeding habitat may also
influence Bicknell’s Thrush populations. For example, an increase in canopy
height or loss of subcanopy structure could reduce availability of suitable
nest sites. Such changes are incremental in our study area, when and where
they occur. In general, White Mountain spruce-fir forests grow slowly,
experience regular natural disturbance, and are not subject to adverse silvicultural
treatments, such as precommercial thinning (Chisholm 2005).
Atmospheric pollution has also been cited as a potential threat to Bicknell’s
Thrush (Atwood et al. 1996). The White Mountains receive high levels
of acid deposition (Ollinger et al. 1993), which leaches calcium from soils and
foliage (DeHayes et al. 1999). In Europe, depletion of calcium-rich invertebrate
prey from acidified forests has been implicated in egg-laying irregularities
and reduced reproductive success in forest passerines (Graveland and
vanderWal 1996, Graveland et al. 1994). In a study of Hylocichla mustelina
Gmelin (Wood Thrush) in the eastern United States, Hames et al. (2002) found
a negative relationship between acid rain and breeding probability, especially
at upper elevations where soils are thin and poorly buffered.
Birds that nest in acidified ecosystems face an elevated risk of mercury
contamination because acidic environments promote the conversion of inorganic
mercury (Hg) to toxic methylmercury (MeHg) (Miskimmin et al. 1992).
The large amount of mercury deposited on northeastern mountains (Miller et
al. 2005) compounds the threat to high-elevation insectivores, such as Bicknell’s
Thrush. Rimmer et al. (2005c) found elevated MeHg levels, with highest
concentrations in older males. Studies of aquatic birds have shown that mercury
contamination can limit avian reproduction and survival (Chan et al. 2003).
Research is needed to determine toxicity thresholds in Bicknell’s Thrush.
Another class of pollutants, greenhouse gases, may further impair Bicknell’s
Thrush habitat. A warming climate is expected to significantly reduce
or eliminate Balsam Fir forests in the Northeast (Iverson and Prasad 2002).
At upper elevations, Red Spruce and Balsam Fir may be replaced by Betula
alleghaniensis Britt. (Yellow Birch), Fagus grandifolia Ehrh. (American
Beech), and Tsuga canadensis (L.) Carr. (Eastern Hemlock) (Lee et al.
2005). Even a modest temperature increase could confine Bicknell’s Thrush
to the region’s highest peaks (Rodenhouse et al. 2008).
Recreational disturbance is an additional source of concern. Foot traffic
2008 J.D. Lambert, D.I. King, J.P. Buonaccorsi, and L.S. Prout 615
is steadily increasing in the White Mountains (US Forest Service 2005), with
unknown effects on birds. Studies conducted elsewhere have shown that human
intrusions can reduce singing activity (Gutzwiller et al. 1994), influence
nest placement (Knight and Fitzner 1985, Miller et al. 1998), and limit both
density (Mallord et al. 2007) and reproductive success (Murison et al. 2007)
in passerines. Trails may also influence species composition and rates of nest
predation in adjacent forests (Miller and Hobbs 2000, Miller et al. 1998).
Although each of these areas requires focused research, improved
monitoring could contribute to an understanding of limiting factors, while
strengthening the inference of future trend results. We suggest the following
measures be incorporated into a regionally coordinated survey design:
1) random route placement within a discrete, rangewide sampling frame;
2) quantitative evaluation of observer ability and/or greater continuity in
observers from year to year; 3) point-count methods that account for variable
detection rates; and 4) repeat sampling to estimate and track changes
in occupancy. Validation of trail-based surveys is also needed to determine
whether populations monitored along footpaths differ from those located
away from trails.
Finally, stewards of mountain habitat require information on environmental
covariates that may influence Bicknell’s Thrush populations. Conservation
and management efforts would benefit from a better understanding
of the role of: habitat change, climatic change, mercury exposure, calcium
availability, predation risk, hiker impacts, and changes in the availability of
wintering habitat. Field measurements and GIS models of these variables
should be integrated into the design of future monitoring.
Bicknell’s Thrush has received high conservation priority ranks on the
basis of its rarity, limited distribution, and reviews of existing and potential
threats. The lack of meaningful trend information has hampered conservation
status assessments for the species. The detection of negative trends in
the White Mountain National Forest between 1993 and 2003 underscores the
importance of efforts to conserve Bicknell’s Thrush and improve monitoring
of high-elevation birds in the Northeast.
Acknowledgments
The study was funded by the White Mountain National Forest, with additional
assistance from the Stone House Farm Fund of the Upper Valley Community Foundation
and friends and trustees of the Vermont Institute of Natural Science. We gratefully
acknowledge the Audubon Society of New Hampshire and US Forest Service personnel
who conducted the surveys. Laura Deming coordinated the point counts for several
years and provided essential information and support. Kent McFarland prepared Figure
1. We thank Yves Aubry, Jon Bart, Becky Whittam, and an anonymous reviewer, who
provided helpful comments on earlier versions of this manuscript.
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