Association of wintering raptors with Conservation Reserve Enhancement Program grasslands in Pennsylvania
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J. Field Ornithol. 81(4):361–372, 2010 DOI: 10.1111/j.1557-9263.2010.00292.x
Association of wintering raptors with Conservation
Reserve Enhancement Program grasslands
in Pennsylvania
Andrew Wilson,1,4 Margaret Brittingham,2 and Greg Grove3
1
USGS, Patuxent Wildlife Research Center, 12100 Beech Forest Road, Laurel, Maryland 20708-4038, USA
2
409 Forest Resources Building, Pennsylvania State University, University Park, Pennsylvania 16802, USA
3
407 Chandlee Lab, Pennsylvania State University, University Park, Pennsylvania 16802, USA
Received 15 November 2009; accepted 5 August 2010
ABSTRACT. Conservation grasslands can provide valuable habitat resource for breeding songbirds, but
their value for wintering raptors has received little attention. We hypothesized that increased availability of
grassland habitat through the Conservation Reserve Enhancement Program (CREP) has resulted in an increase or
redistribution in numbers of four species of raptors in Pennsylvania since 2001. We tested this by analyzing winter
raptor counts from volunteer surveys, conducted from 2001 to 2008, for Red-tailed Hawks (Buteo jamaicensis),
Rough-legged Hawks (Buteo lagopus), Northern Harriers (Circus cyaneus), and American Kestrels (Falco sparverius).
During that period, numbers of wintering Northern Harriers increased by more than 20% per year. Log-linear
Poisson regression models show that all four species increased in the region of Pennsylvania that had the most and
longest-established conservation grasslands. At the county scale (N = 67), Bayesian spatial models showed that
spatial and temporal population trends of all four species were positively correlated with the amount of conservation
grassland. This relationship was particularly strong for Northern Harriers, with numbers predicted to increase
by 35.7% per year for each additional 1% of farmland enrolled in CREP. Our results suggest that conservation
grasslands are likely the primary cause of the increase in numbers of wintering Northern Harriers in Pennsylvania
since 2001.
RESUMEN. Asociación de rapaces invernales con el programa de desarrollo de reservas
de pastizales para conservación en Pennsylvania
La conservación de pastizales pueden proveer un recurso de habita valioso para la reproducción de aves passerinas,
pero su valor para aves rapaces durante el invierno ha recibido poca atención. Hipotetisamos que el incremento
de la disponibilidad de hábitat a través programa de desarrollo de reservas para la conservación ha resultado en un
incremento o redistribución en números de cuatro especies de rapaces en Pennsylvania desde el 2001. Pusimos a
prueba esto por medio del análisis de censos de conteo durante el invierno de rapaces realizados por voluntarios,
entre el 2001 y el 2008, para Buteo jamaicensis, Buteo lagopus, Circus cyaneus, y Falco sparverius. Durante este periodo
los números de C. cyaneus incrementaron en más del 20% por año. Un modelo de regresión logı́stica linear de
Poisson mostro que las cuatro especies incrementaron en la región de Pennsylvania que tiene la mayor y más antigua
reserva de conservación de pastizales. A la escala del paı́s (N = 67), modelos espaciales Bayesianos mostraron que las
tendencias temporales y espaciales de las poblaciones de las cuatro especies estuvieron positivamente correlacionadas
con la cantidad de pastizal conservado. Esta relación fue particularmente fuerte para C. cyaneus, con predicciones
de incremento en un 35.7% por año por cada 1% de tierra de fincas que se unan al DRPCP. Nuestros resultados
sugieren que la conservación de pastizales es la primera y más probable causa responsable del incremento de la
estancia invernal del C. cyaneus en Pennsylvania desde el 2001.
Key words: American Kestrel, conservation grassland, CREP, CRP, Northern Harrier, population trends, raptor
Grassland obligate bird populations have been of agricultural practices in the United States,
declining across North America for the past sev- the Conservation Reserve Program (CRP) was
eral decades (Vickery and Herkert 2001, Sauer introduced in 1985 (Isaacs and Howell 1988).
et al. 2008). Specific causes of these declines The CRP is a voluntary land retirement program
likely vary among species, but are generally where farmers convert erodible arable land to
attributable to the long-term decline in availabil- perennial cover, typically grass-legume mixes,
ity of grassland habitat (Peterjohn 2003). Due for contract periods of 10–15 years in return
to concerns about the environmental impacts for payment. The CRP has resulted in millions
of hectares of temporarily restored grasslands
across the United States that has benefited
4
Corresponding author. Email: amwilson@usgs.gov grassland birds (Johnson and Igle 1995, Ryan
C 2010 The Authors. Journal of Field Ornithology
C 2010 Association of Field Ornithologists
361362 A. Wilson et al. J. Field Ornithol. Fig. 1. CREP regions in Pennsylvania and percentage of arable land by program year and program area. Source of CREP acreage data: USDA (2008). Area of arable land estimated from Landsat Enhanced Thematic Mapper (ETM) derived land cover data for circa 2000. et al. 1998, Swanson et al. 1999). Enrollment in River Basin (USR) and Ohio River Basin in CRP was low in Pennsylvania due to low rental 2003 and 2004, respectively (Fig. 1). rates (Klinger 2008), with only 38,000 hectares Many species of raptors in North America enrolled by 1997 (USDA 1998). are associated with grasslands, but the effects The 1997 Farm Bill promoted wildlife con- of conservation programs on raptor popula- servation as coequal with soil conservation as an tions have received little attention. In a sample objective of CRP and authorized the Conserva- of 64 peer-reviewed studies of the effects of tion Reserve Enhancement Program (CREP). CRP/CREP on birds (Wilson 2009), only six CREP allowed management of grasslands to included raptors and only one (Littlefield and maximize benefits for wildlife, such as more Johnson 2005) focused specifically on them. stringent restrictions on mowing (Wentworth We used data from a citizen science study, the et al. 2010). In Pennsylvania, CREP was es- Pennsylvania Winter Raptor Survey (WRS), to tablished in 2000 and more than 80,000 ha of test the hypothesis that increased availability of conservation grasslands were enrolled by 2007 grassland habitat due to CREP enrollment has (USDA 2008). The program was implemented resulted in an increase or redistribution in num- in three phases, with the earliest enrollment in bers of four species of raptors in Pennsylvania, the Lower Susquehanna River Basin (LSR), fol- including Red-tailed Hawks (Buteo jamaicensis), lowed by expansion to the Upper Susquehanna Rough-legged Hawks (Buteo lagopus), Northern
Vol. 81, No. 4 Wintering Raptors on Conservation Grasslands 363
Table 1. Pennsylvania WRS coverage and effort and total counts of four species from 2001 to 2008.
Survey coverage Total birds counted
Year Counties Hours Northern Harrier Red-tailed Hawk Rough-legged Hawk American Kestrel
2001 45 253 24 1141 44 343
2002 56 314 30 1399 21 392
2003 61 392 28 1182 99 357
2004 62 514 94 2052 341 265
2005 63 494 70 2610 200 433
2006 61 478 80 2184 93 488
2007 61 505 107 2218 87 511
2008 62 504 133 2390 88 510
Harriers (Circus cyaneus), and American Kestrels was not overly restrictive to ensure adequate
(Falco sparverius). volunteer participation, participants were asked
Because raptor numbers in winter may be in- to standardize survey routes, durations, and the
fluenced by weather, especially snowfall (Grove number and timing of stops between years.
2010), we also examined snowfall data to Survey coverage increased during the first
help differentiate between the possible effects 3 years of the survey, but was constant from 2004
of snowfall and CREP enrollment. Although to 2008. Total survey hours increased from 253
relationships between winter raptor numbers in 2001 to approximately 500 each year from
and short-term and localized weather events are 2004 to 2008 (Table 1). Surveys were conducted
complex, previous analysis of these WRS data in all 67 counties of Pennsylvania, but in no
showed that numbers of American Kestrels in more than 63 counties in any 1 year (Table 1).
Pennsylvania were lower during winters with The mean number of routes per county was 2.0.
more snowfall than average, numbers of Rough- From 2004 to 2008, WRS survey routes covered
legged Hawks increased in snowy winters, and more than 12,500 km per year.
numbers of the other two species showed no Routes were restricted to one county, so coun-
obvious effect of weather (Grove 2010). ties were the sampling unit used in our analysis.
The large size of the sampling units (mean area of
METHODS counties = 1780 km2 ) is justified for wintering
raptors because they are not territorial and
Surveys. The Pennsylvania WRS was es- appear to be very mobile during and between
tablished in 2001 by the Pennsylvania Society for winters (Preston and Beane 1993, MacWhirter
Ornithology to assess mid-winter distributions and Bildstein 1996, Bechard and Swem 2002,
of raptors and vultures in Pennsylvania and Smallwood and Bird 2002).
detect long-term trends in abundance (Grove Land cover and snowfall. We estimated
2010). Roadside counts were conducted by the extent of each major land cover type in
volunteers once per winter during the period each county using Landsat 7 ETM land cover
from 15 January to 15 February. Routes were 2000 (from 1999 to 2002) data in ArcGIS
nonrandom, chosen by the volunteers, and (ESRI 2004). Grassland and arable land cover
ranged in length from 16 to 160 km. Counts types were combined to provide an estimate of
were conducted between mid-morning and mid- farmland extent (Table 2) at the start of the study
afternoon, and foggy, windy, rainy, and snowy period. We estimated CREP enrollment rates
days were avoided. During surveys, there was as a percentage of the total farmland for each
no restriction on the number of times observers county and program year. CREP enrollment
could stop and survey for raptors. In addition, data were obtained from the USDA monthly
all raptors observed, regardless of their distance contract reports (USDA 2008) by summing
from roads, were counted. WRS guidelines rec- the number of hectares enrolled in the follow-
ommended that surveys be conducted by teams ing grassland conservation practices: CP01—
of drivers and spotters. Although the protocol introduced grasses and legumes (cool season364 A. Wilson et al. J. Field Ornithol.
Table 2. Land-cover types and CREP grassland enrollment by CREP region. Land-cover data from Landsat
7 ETM data, circa 2000. CREP enrollment rates are the mean percentages of land in CREP by the end of
2006 for counties in each region.
Percent cover by region
CREP region Counties Urban (c.2000) Farmland (c.2000) CREP (2006)
Lower Susquehanna River Basin 20 4.2 45.7 2.48
Upper Susquehanna River Basin 23 2.4 22.6 0.67
Ohio River Basin 16 7.5 33.1 0.31
Delaware Valley (no CREP) 8 23.1 26.7 0
Pennsylvania total 67 6.8 32.9 0.94
grasses), CP02—native grasses (warm season and topography among regions, it is a useful
grasses), CP10 (vegetative cover—grass already exploratory step for evaluating whether raptor
established), and CP21—filter strips (grasses). populations trends differed among the LSR,
Because most CREP grasslands are sown during where CREP enrollment was earliest and most
the spring, land enrolled in CREP during the substantial, the two regions where the program
previous year was assumed to be available to was subsequently expanded (USR and Ohio),
raptors as foraging habitat by the time of surveys and the eight counties of the Delaware Valley
conducted the following January and February. where no land has been enrolled in CREP
Cumulative snowfall totals for January of (Fig. 1).
each year in 10 climatic regions of Pennsylvania Winter raptor population trends were also
were obtained from PASC (2008). These totals modeled with respect to rates of CREP enroll-
were then matched to the (4–11) counties that ment at the county scale. WRS counts were
compose each climatic region. Although there modeled as Poisson random variables using
is some spatial and temporal mismatch between Bayesian models, allowing incorporation of spa-
snowfall data and bird survey data, we believe tial structure and “nuisance” variables such as
that the available snowfall data are a good proxy environmental factors (Thogmartin et al. 2006).
for the severity of the winter until the time of This modeling method is increasingly used for
the bird counts, most of which were conducted analyzing the results of large-scale bird popula-
from late January to mid-February. The number tion surveys, such as the Breeding Bird Survey
of wintering raptors in Pennsylvania could also (Link and Sauer 2002, Thogmartin et al. 2006)
be influenced by weather outside the state, but, and Christmas Bird Count (Link et al. 2006,
in the absence of information about the origins Link and Sauer 2007).
of raptors in Pennsylvania, accounting for short- We modeled the expected count () of each
term (within winter) weather effects at larger species in each county (i) and year ( j) as follows:
geographic scales was not possible.
Population trends. Population trends for ln[i j ] = + ␥i ( j − j1 )
Northern Harriers, Red-tailed Hawks, Rough- p
legged Hawks, and American Kestrels were es- + ik xi jk + ␣ j + i j + i j + εi j
timated for the years from 2001 to 2008 using k=1
program TRends and Indices for Monitoring
data (TRIM). TRIM statistical software is de- where is the intercept, j 1 is the first year
signed to analyze time-series of counts with (2001), ␥ i is the linear trend,  ik are effects of p
missing observations, using Poisson regression environmental covariates x ijk , ␣ j are random year
(Pannekoek and van Strien 2001). Because effects, ij are random county specific effects,
CREP enrollment differed in both scale and ij are survey effort effects, and ε ij are Poisson
timing among the three CREP regions (Fig. 1), errors. Covariates included CREP enrollment
we calculated separate trends for each region rates, percent of county in urban land use,
by including region as a covariate. Although percent in farmland and grassland, and the
this analysis is coarse and does not correct cumulative January snowfall for the climactic
for substantial differences in land use, climate, region where the county was located. CREP andVol. 81, No. 4 Wintering Raptors on Conservation Grasslands 365
snowfall covariates were year-dependent and We conducted at least 10,000 iterations of each
land cover covariates were constant through model as a burn-in, running the MCMC pro-
time. We included two broad land cover covari- cess until stabilization occurred in visual trace
ates in the model to account for effects of varia- plots, indicating model convergence. The burn-
tion in amounts of winter raptor habitat (farm- in samples were discarded and a further 100,000
land) and nonhabitat (urban) among counties. iterations conducted.
The two land cover covariates and snowfall were The full model for each species was compared
standardized by subtracting the mean and then to models without the spatial effect ( ij ) and
divided by standard deviation to improve model the effort effect ( ij ). The need to incorporate
convergence (Gilks and Roberts 1996). these effects was our main justification for us-
After Link and Sauer (2007), we included a ing Bayesian models rather than more simple
survey effort effect ( ij ) to correct for variation frequentist general linear models. The most par-
in effort among counties and years. The effect of simonious among the “full,” “nonspatial,” and
the number of hours spent counting raptors () “no effort-effect” models was selected using the
on the number of raptors counted is modeled Deviance Information Criterion (DIC), which is
as: the Bayesian equivalent of Akaike’s Information
¯ (1/B) . Criterion (AIC; Burnham and Anderson 2002).
f () = (/)
A lower DIC indicates better model fit.
As an inverse power function, B = 1 would Predicted birds/hour values for a hypotheti-
indicate a linear relationship between effort cal average county were calculated within the
and counts, B > 1 would suggest diminishing MCMC step of the model by back-transforming
returns, and B < 1 would suggest increasing the model with annual mean values of survey
returns with extra effort. Link and Sauer (2007) effort, snowfall, and CREP enrollment across
included a second parameter to allow examina- the 67 counties. Land cover metrics were set to
tion of the effect of effort on counts of Car- the average. However, because these metrics had
olina Wrens (Thryothorus ludovicianus) during previously been standardized (to facilitate model
Christmas Bird Counts to reach an asymptote, conversion), their average was zero and, there-
but found no evidence that the more simple fore, including them in the predictive model was
formulation shown above was not adequate. not necessary. We measured model goodness-of-
We assumed that increased effort would yield fit with the posterior predictive P-value (Gelman
diminishing returns, primarily because the best et al. 1996). A P-value close to 0.0 or 1.0
areas for raptors would be surveyed first and, indicates the data do not agree with the proposed
therefore, additional survey routes would likely model; a value near 0.5 indicates an adequate
produce lower raptor counts. fit. The significance of each parameter was
We used a random route-specific effect with determined by 95% credible intervals, which
a Gaussian conditional autoregressive (CAR) are the Bayesian equivalent of 95% confidence
model to incorporate spatial autocorrelation in intervals (Banerjee et al. 2004).
our data. For data gathered over geographic To evaluate the effect of CREP on raptor pop-
areas, such as counties, CAR models are widely ulation trends, we compared predicted trends for
used (Thogmartin et al. 2004, 2006, Jin et al. areas/counties with higher than average levels of
2005). These models assume spatial correlation CREP enrollment and those with no land en-
between adjacent samples; in our case, coun- rolled. These predictions were for a hypothetical
ties. We fitted the model using Markov Chain “average” county—equivalent to the statewide
Monte Carlo (MCMC) methods in WinBUGS average in terms of environmental covariates
(Speigelhalter et al. 2004). Bayesian statistics (percent farmland, percent urban, and January
allows prior knowledge to be used in estimat- snowfall) and survey effort. This was calculated
ing parameters, but, because we had no prior by exponentially back-transforming the model
information, we used vague prior distributions equations to provide an estimate of birds per
(Link and Sauer 2002) to begin the MCMC hour for each year. For a scenario where no land
process. Parameters for fixed effects (environ- is enrolled in CREP, the CREP term was held
mental variables and time trend) were assigned at zero in each year. For the scenario where a
normal distributions with a mean of 0.0 and high percentage of land is enrolled in CREP, a
variance of 100 (precision = 1/variance = 0.01). CREP term corresponding to the county at the366 A. Wilson et al. J. Field Ornithol.
Fig. 2. Wintering hawk population trends (and 95% CI) for Pennsylvania (all PA) and CREP regions for
2001–2008. Percentage annual change is the linear trend from log-linear models with Poisson errors. LSR =
Lower Susquehanna River Basin CREP region, USR = Upper Susquehanna River Basin, and Ohio = Ohio
River Basin. There is no CREP in eight counties of the Delaware Valley.
90th percentile of enrollment (8th highest) for (Fig. 2). Counts of Rough-legged Hawks and
each year was used. Note that the “high” CREP American Kestrels also declined significantly in
enrollment rates are only slightly higher than the Delaware Valley, but there was no evidence
the median (10th/11th ranked) among the 20 that linear trends differed among the three
counties of the LSR. CREP areas (Fig. 2).
Bayesian model evaluation. The
RESULTS
MCMC errors of the seven model parameters
were generally less than 5% of the standard
Trends from log-linear Poisson Regres- deviation of the parameter estimates
sion. From 2001 to 2008, counts of Northern (Spiegelhalter et al. 2004), the only exceptions
Harriers increased by an average of 20% per being errors for the parameter for linear trend
year (Fig. 2). We found a significant increase for three of the four species (Table 3) that
in the LSR CREP area, no significant change were 6–8%. For all four species of raptors,
in the USR and Ohio River Basin, and a the full model that included both spatial and
significant decrease in the Delaware Valley where effort effects provided a better fit (lower DIC)
no land was enrolled in CREP. Counts of Red- than models that did not (Table 4). The effect
tailed Hawks increased significantly in the LSR of observer effort was significant for all four
CREP area from 2001 to 2008, but did not species (Table 4). The correction factor applied
change significantly in the USR CREP area and to counts due to the observer effect resulted
decreased significantly in the Ohio River Basin in a downward adjustment of estimates forVol. 81, No. 4 Wintering Raptors on Conservation Grasslands 367
Table 3. Parameter estimates from Bayesian spatial models of winter raptor counts in Pennsylvania from 2001
to 2008. The 95% credible intervals are given by the 2.5 and 97.5 percentiles.
Percentiles
Mean SD MCMC error 2.5% 97.5%
Northern Harrier
(intercept) −2.669 0.293 0.013 −3.265 −2.056
(effort) 1.966 0.947 0.015 1.205 3.899
ß (trend) 0.046 0.068 0.003 −0.100 0.173
ß (snowcover) −0.219 0.181 0.005 −0.602 0.095
ß (urban) −0.095 0.261 0.006 −0.634 0.393
ß (farmland) 0.050 0.190 0.005 −0.334 0.412
ß (CREP) 0.305 0.059 0.002 0.193 0.423
Red-tailed Hawk
(intercept) 1.749 0.139 0.008 1.431 2.015
(effort) 1.356 0.088 0.003 1.206 1.550
ß (trend) 0.015 0.033 0.002 −0.049 0.090
ß (snowcover) 0.030 0.040 0.001 −0.048 0.107
ß (urban) 0.033 0.123 0.006 −0.207 0.274
ß (farmland) 0.289 0.095 0.005 0.099 0.477
ß (CREP) 0.042 0.020 0.001 0.002 0.082
Rough-legged Hawk
(intercept) −2.105 0.729 0.046 −3.731 −0.741
(effort) 2.003 0.824 0.016 1.240 3.981
ß (trend) 0.035 0.174 0.011 −0.321 0.403
ß (snowcover) 0.193 0.108 0.003 −0.019 0.408
ß (urban) −0.055 0.254 0.007 −0.563 0.442
ß (farmland) 0.005 0.197 0.006 −0.379 0.399
ß (CREP) 0.179 0.055 0.002 0.075 0.288
American Kestrel
(intercept) 0.068 0.193 0.010 −0.337 0.435
(effort) 1.419 0.127 0.002 1.211 1.706
ß (trend) −0.044 0.046 0.002 −0.141 0.048
ß (snowcover) −0.110 0.062 0.001 −0.231 0.013
ß (urban) −0.084 0.193 0.007 −0.467 0.294
ß (farmland) 0.358 0.141 0.006 0.073 0.624
ß (CREP) 0.059 0.024 0.001 0.013 0.106
counties and years where effort was less than the ranged widely (dependent on hours of effort),
average (7.2 h), whereas counts were adjusted but about half of all corrections were modest
upwards for counties and years where effort (0.6 and 1.2). Inclusion of the effort effect did
was greater than the average. Correction factors little to change the estimates of birds per hour for
Table 4. Goodness of fit of Bayesian models (Table 3) and changes in the DIC for models of winter raptor
numbers. The full models included effort effects, to account for changes in survey effort between years and
counties, and spatial effects to account for spatial autocorrelation.
DIC full model DIC
a
Goodness of fit DIC No effort effect No spatial effect
Northern Harrier 0.425 1025 20.0 38.2
Red-tailed Hawk 0.745 3107 17.9 37.0
Rough-legged Hawk 0.422 1170 9.4 58.5
American Kestrel 0.464 1996 57.9 141.5
a
Posterior predictive P-value (Gelman et al. 1996).368 A. Wilson et al. J. Field Ornithol. Fig. 3. Population trends (and 95% credible intervals) for wintering raptors in Pennsylvania from 2001 to 2008 showing changes in estimated trend as a result of including variable effort effects in the model. Northern Harriers and Rough-legged Hawks, more farmland (positive parameter estimates but resulted in reducing estimates for the earlier for farmland; Table 3), and significantly higher years of the surveys and increasing estimates for for Red-tailed Hawks and American Kestrels Red-tailed Hawks and American Kestrels during (95% credible intervals did not overlap zero). the later years (Fig. 3). This correction suggests Counts of all four species were positively and that the increase in survey effort through the significantly associated with higher rates of time series was directed into areas where raptors CREP enrollment (positive parameter estimates, were less likely to be encountered, supporting 95% credible intervals did not overlap zero; our assumption that the best areas for wintering Table 3). The effect of CREP was strongest for raptors were more likely to be chosen first by Northern Harriers, with the parameter estimate surveyors. of 0.305 translating into a 35.7% increase in CREP effect and other environmental Northern Harrier counts for each additional covariates. We did not find strong support in 1% of farmland enrolled (exponential back- the model for snowfall totals in January having transformation). The result of this effect over the a significant effect on raptor counts because 8-year period was that the predicted counts for 95% credible intervals of the parameter estimate a county with high rates of CREP enrollment overlapped zero (Table 3). Counts of all four increased considerably faster than those for a species of raptors were higher in counties with county with no land enrolled in CREP (Fig. 4).
Vol. 81, No. 4 Wintering Raptors on Conservation Grasslands 369
Fig. 4. Estimated population trends (and 95% credible intervals) for wintering raptors in Pennsylvania from
2001 to 2008 for scenarios where there was no CREP, and where enrollment was higher than at present (5%
of farmland enrolled by 2007).
The predicted effect of high CREP enrollment of Northern Harriers in Pennsylvania may be
on trends of the other three species was positive, almost entirely attributable to CREP. Although
but equivocal (Fig. 4). the results are correlational, the association be-
tween CREP enrollment and higher numbers of
Northern Harriers was correlated in both space
DISCUSSION
(counties) and time (years), providing a more
Our analysis of WRS data revealed increasing compelling case for a causative effect than if
wintering populations of Red-tailed Hawks and, the associations were merely correlated in time.
especially, Northern Harriers in Pennsylvania In the only other study of use of CRP fields
during the period from 2001 to 2008. Although by wintering raptors, Littlefield and Johnson
long-term monitoring program data can be sub- (2005) found that they were favored foraging
ject to observer effects, especially among novice sites for Northern Harriers in Texas.
observers (Link and Sauer 1998), we consider The increase in numbers of wintering North-
the magnitude of increase in Northern Harrier ern Harriers in Pennsylvania could be due to
counts to be too large to be attributed to the po- range shifts of individual birds, changes in
tential effects of increasing observer experience. mortality or recruitment, or some combina-
Our model suggests that the increase in numbers tion of these factors. Breeding populations in370 A. Wilson et al. J. Field Ornithol.
Pennsylvania and elsewhere could have in- Our analysis revealed a decrease in numbers
creased, but BBS data provide no evidence of of American Kestrels in Pennsylvania during the
such increases (Sauer et al. 2008). Christmas winters of 2003 and 2004. One factor that may
Bird Count data indicate that numbers of win- have contributed to that temporary decline is
tering Northern Harriers in the central United the West Nile Virus, which was first detected in
States fluctuate with the climatic influences of Pennsylvania in 2000 (Medica et al. 2007). A
the El Niño–Southern Oscillation (Kim et al. 44% decline in a breeding population of Amer-
2008), but there was no evidence of an over- ican Kestrels in southeast Pennsylvania between
all trend. Thus, raptors may exhibit weather- 2002 and 2004 was likely due to increased
induced shifts in winter ranges that can be large- mortality caused by West Nile Virus (Medica
scale, but short-term. However, all four raptors et al. 2007).
in our study have shown significant northwards Our study provides evidence that, at a coarse
shifts in latitudinal center of abundance in scale, population trends of wintering raptors in
North America since 1966, with climate change Pennsylvania during the period from 2001 to
thought to be a cause (National Audubon 2008 were positively correlated with the amount
Society 2009). The Atlantic coast states from of land enrolled in CREP, but our results also
Delaware to Florida support high numbers of suggest that the effect on numbers of Red-tailed
Northern Harriers during the winter (National Hawks, Rough-legged Hawks, and American
Audubon Society 2002) so a northward range Kestrels was modest. However, the strong pos-
shift in a small proportion of that population itive spatial and temporal correlation between
could result in a disproportionately large in- CREP enrollment and numbers of Northern
crease in the number wintering in Pennsylvania. Harriers is consistent with our hypothesis that
A redistribution of birds within the state could CREP has resulted in an increase in the numbers
also have been responsible for the positive cor- of this species wintering in Pennsylvania. Such
relation in numbers of Northern Harriers with increases may not translate into an increase in
CREP enrollment. However, we found a large population size because it is not known to what
overall increase in Northern Harrier numbers extent winter habitat and over-winter mortality
during the period from 2001 to 2008 so, for are limiting in these raptors. However, increases
this species at least, a redistribution of wintering in the number of Northern Harriers wintering
birds within the state would not explain the in Pennsylvania are encouraging, especially given
strong correlation between bird numbers and long-term declines at the national scale (Sauer
CREP enrollment. et al. 2008).
Red-tailed Hawks (Preston and Beane 1993)
and American Kestrels (Smallwood and Bird
2002) are also found in open grassy fields, ACKNOWLEDGMENTS
where they prey mainly on mammals during the First and foremost, we thank the more than 200 vol-
winter. However, these species are also habitat unteers who conducted WRSs in Pennsylvania. Without
generalists, likely explaining why the effect of their dedication and hard work, our study would not have
been feasible. We are indebted A. Wilson’s thesis com-
increasing land enrolled in CREP was modest mittee, D. Diefenbach, M. Haran, and W. Tzilkowski,
for these species compared to that for Northern for their encouragement and advice. W. Thogmartin
Harriers, a species more closely tied to grassland provided statistical advice. Funding was provided by the
habitat. CREP fields may also provide suitable Pennsylvania Game Commission, Intercollege Graduate
habitat for Rough-legged Hawks, but low num- Degree Program in Ecology (PSU) and School of Forest
Resources (PSU). The editors and two anonymous re-
bers in Pennsylvania during most winters make viewers provided many useful suggestions for improving
it difficult to determine the possible importance the manuscript.
of these field for these hawks. Red-tailed Hawks,
Rough-legged Hawks, and American Kestrels
often hunt from perches, but CREP fields in LITERATURE CITED
Pennsylvania are small (mean = 7.7 ha; USDA
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