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2019 SOUTHEASTERN NATURALIST 18(2):192–201
Habitat Preferences of Nesting Southeastern American
Kestrels in Florida: The Importance of Ground Cover
Karl E. Miller1,*, Ryan Butryn1,2, Erin Leone1, and Jason A. Martin1,3
Abstract - Habitat associations for Falco sparverius (American Kestrel) have been quantified
for agricultural landscapes dominated by pastures and fields, but little is known about
the species’ habitat requirements in natural plant communities such as forests, savannas,
and grasslands. Prescriptions for habitat management for the threatened F. s. paulus (Southeastern
American Kestrel) in sandhills remain unclear. We assessed how habitat features
affected occupancy rates and nest success of Southeastern American Kestrels on 4 conservation
lands in peninsular Florida. We assessed habitat relationships at 3 spatial scales (patch,
territory, landscape) around 58 nest boxes. We identified a reduced habitat-patch model with
1 variable (percent grass cover) as the best fit for predicting Southeastern American Kestrel
occupancy, but none of the habitat models predicted nest success better than the null model.
Occupied patches averaged more grass cover (52%), and unoccupied patches averaged relatively
little grass cover (32%). Habitat characteristics within nest box territories occupied
by Southeastern American Kestrels (i.e., open tree canopy with few woody shrubs and a
graminoid-dominated low groundcover) were consistent with ecological reference conditions
for sandhills and habitat conditions recommended for other fire-dependent bird species
of conservation interest. The loss of suitable foraging habitat (e.g., open ground cover) has
received little attention in regional or continental efforts to arrest population declines of
the American Kestrel. Additional effort toward maintaining suitable groundcover in native
pyrogenic plant communities for Southeastern American Kestrels appears to be warranted.
Introduction
Ongoing population declines of Falco sparverius L. (American Kestrel) have
been documented throughout eastern North America (Farmer and Smith 2009,
Sauer et al. 2012, Smallwood et al. 2009a) and especially in the southeastern US
(Hoffman and Collopy 1988). American Kestrel habitat associations have been
quantified in agricultural landscapes dominated by pastures, farms, and fields
(e.g., Rohrbaugh and Yahner 1997, Smallwood and Collopy 2009, Smallwood
and Wargo 1997, Toland and Elder 1987), but little is known about American
Kestrel habitat requirements in natural plant communities such as forests, savannas,
and grasslands.
Falco sparverius paulus Howe and King (Southeastern American Kestrel),
a non-migratory subspecies, was once widely distributed throughout 7 southeastern
states but today is patchily distributed in Florida and the coastal plain of
1Fish and Wildlife Research Institute, Florida Fish and Wildlife Conservation Commission,
1105 Southwest Williston Road, Gainesville, FL 32601. 2Current address: 579 South Willard
Street, Burlington, VT 05401. 3Current address: Kleenco Environmental, 8239 North
State Road 9, Alexandria, IN 46001. *Corresponding author - karl.miller@myfwc.com.
Manuscript Editor: Frank Moore
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neighboring states (FWC 2003, Schneider et al. 2010, Smallwood and Bird 2002).
Range contractions and population declines of the subspecies have been attributed
to habitat changes associated with fire suppression and conversion of Pinus palustris
Mill. (Longleaf Pine) sandhills to pine plantations (Gault et al. 2004, Hoffman
and Collopy 1988). Roadside surveys in Florida during the 1980s recorded more
Southeastern American Kestrels in landscapes that included remnant patches of
sandhills than in landscapes without sandhills (Bohall-Wood and Collopy 1986).
Breeding populations still occur in an old-growth Longleaf Pine forest in the
Florida panhandle (Blanc and Walters 2008, Gault et al. 2004) and less commonly
in second-growth pine forest in peninsular Florida (FWC 2003). However, no
quantitative information is available on the habitat requirements of Southeastern
American Kestrels in sandhills or other native plant communities. Prescriptions
for habitat management will remain unclear until habitat relationships are assessed
within plant communities and across landscapes.
We compared nest box use of Southeastern American Kestrels in sandhills at
different spatial scales with a structured hypothesis-testing approach. Assessment
of habitat relationships across multiple spatial scales can improve understanding of
limiting factors (Mayor et al. 2009). Our objective was to determine what is most
important in determining occupancy of nest boxes and nest success by assessing
vegetation structure within the immediate habitat patch, the amount of suitable
habitat within the nesting territory, and the pattern and configuration of habitat
patches in the landscape around the territory. Our ultimate objective was to provide
quantitative habitat management guidelines to land managers.
Study Area
We assessed habitat relationships for Southeastern American Kestrels at 58 nest
boxes on 4 public conservation lands in north-central Florida: Ichetucknee Springs
State Park (Suwannee and Columbia counties), Camp Blanding Training Center
and Wildlife Management Area (Clay County), Gold Head Branch State Park (Clay
County), and Ordway–Swisher Biological Station (Putnam County). We selected
these public lands because they were dominated by sandhills and had long-standing
programs (established in 2002 or earlier) of providing nest boxes for Southeastern
American Kestrels. Nest boxes were installed in sandhills primarily by FWC and
the University of Florida during the 1990s.
Longleaf Pine sandhill is a pyrogenic, xeric, upland plant community composed
of widely spaced pines that form an open canopy, a sparse midstory layer of xerophytic
Quercus spp. (oaks), a poorly developed shrub layer that includes oaks
and Serenoa repens (W. Bartram) Small (Saw Palmetto), and herbaceous ground
cover of grasses and forbs (Myers 1990, Platt et al. 1988, Provencher et al. 2002).
Once found throughout the southeastern US, Longleaf Pine sandhills have declined
primarily due to fire suppression and land-use conversion (Ware et al. 1993). The
vegetative structure of this subclimax community is naturally maintained by lowintensity
fires at frequent intervals, described variously as 1–3 y (FNAI 2010) and
2–5 y (Provencher et al. 2002). Prescribed burning occurred at our study sites at
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a similar frequency, although intervals varied locally, and fire histories were not
available for all sites. The Longleaf Pine forests were uneven-aged, with a significant
component of trees older than 70 y and scattered relict trees aged 125–250 y.
Other plant communities in the landscape included mesic pine flatwoods, wetlands,
and hammocks.
Methods
We typically visited nest boxes once every 7–10 d during March–June 2009
and inspected nest boxes with either an aluminum extension ladder or a video
camera mounted on a telescoping fiberglass pole. We made more-frequent visits
close to the anticipated hatching date to ensure accurate estimation of hatching
and fledging dates, with incubation and nestling periods of 30 d and 28 d, respectively,
used as guidelines (Smallwood and Bird 2002). For this analysis, we
defined occupancy as evidence of at least 1 egg having been laid and success as
evidence of at least 1 nestling having fledged. We considered nestlings known
to be alive ≤7 d prior to fledging to have fledged (Smallwood and Collopy 2009,
Steenhof and Newton 2007).
We used the Breeding Biology Research and Monitoring Database framework to
sample vegetation within a ~1-ha habitat patch around each nest box (T.E. Martin,
Montana Wildlife Cooperative Research Unit, University of Montana, Missoula,
MT, unpubl. data). We established 4 vegetation sampling plots at each nest box. We
centered 1 plot on the nest box, while remaining plots were located 30 m from the
central plot at 120° intervals. We recorded percent cover of bare ground, grass, and
shrubs; maximum height of ground cover; and maximum height of horizontal visual
obscurity (robel pole) in a 5 m × 5 m area centered on each plot. We measured
several aspects of forest structure, including counting snags, pines, and hardwoods
in 2 size classes (8–15 cm diameter at breast height [dbh] and >15 cm dbh), and determining
median height of pines and hardwoods in a 0.04-ha circle (11.3-m radius)
centered on each plot. At each plot, we also estimated percent canopy closure with
a concave densiometer and basal area of pine and hardwood trees using a prism.
We averaged these vegetation variables across plots associated with each nest box
to determine composite values for that habitat patch.
We used the Florida Cooperative Land Cover Map version 2.3 (FWC and
FNAI, Tallahassee, FL) and ArcGIS Desktop (version 10.3, ESRI, Redlands, CA)
to quantify habitat variables surrounding nest boxes. We used FragStats version 4
(McGarigal et al. 2012) to measure landscape attributes at 2 scales (500-m buffer
[78.5 ha] and 1-km buffer [314 ha]) around nest boxes. The smaller scale encompassed
the entirety of a typical American Kestrel territory, while the larger scale
effectively encompassed a landscape several times larger than a typical territory
(Bird and Palmer 1988; K.E. Miller, pers. observ.; Smallwood and Bird 2002).
We measured the total area, number of patches, and mean patch size of breeding
habitat (FNAI classification categories: 1240 Sandhill, 1231 Upland Pine). Southeastern
American Kestrels sometimes avoid nest boxes near dense woodlands
(K.E. Miller, pers. observ.; Wilmers 1983), so we also measured the distance to
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unsuitable woodland habitat (1110 Upland Hardwood Forest, 1111 Dry Upland
Hardwood Forest, 1112 Mixed Hardwoods, 1120 Mesic Hammock, 1122 Prairie
Mesic Hammock, 1123 Live Oak, 1124 Pine-Mesic Oak, 1140 Slope Forest, 1150
Xeric Hammock, 1220 Upland Mixed Woodland, 1311 Mesic Flatwoods, 1400
Mixed Hardwood–Coniferous, 1410 Successional Hardwood Forest, 183231 Hardwood
Plantations). In addition, we calculated Edge Density (ArcGIS Desktop,
version 10.3), which is a measure of habitat interspersion.
We constructed all models in SAS 9.2 (SAS Institute Inc., Cary, NC). We used
PROC CORR to identify highly correlated (>.0.7) habitat-patch variables and then
reduced the number of variables in the habitat-patch structure model from 15 to
9 by removing some of the correlated variables. When winnowing down a list of
highly correlated variables, we retained those parameters that habitat managers
could most easily interpret. We then explored combinations of the 9 habitat-patch
structure variables using PROC LOGISTIC, with the SCORE option, to identify the
most influential variables; the top-performing model from this exercise had 1 variable
(percent grass cover). For each response (occupancy and success), we tested
7 competing models against a null model: habitat patch structure, reduced habitatpatch
structure (percent grass cover), habitat amount (500-m scale), habitat amount
(1-km scale), habitat distance, habitat pattern (500-m scale), and habitat pattern
(1-km scale). See Table 1 for the variables included in each model. We used PROC
GLIMMIX to predict occupancy and nest success of Southeastern American
Table 1. Variables included in models used to describe Southeastern American Kestrel occupancy and
nest success.
Models
Habitat Reduced Habitat Habitat Habitat Habitat
patch habitat amount pattern Habitat amount pattern
Variables structure patch 500 m 500 km distance 1 km 1 km
Pine basal area X
Pines 8–15 cm X
Pines > 15 cm X
Oak basal area X
Oaks 8–15 cm X
Oaks >15 cm X
Percent grass X X
Percent shrub X
Maximum height cover X
Habitat area 500 m X
Habitat area 1 km X
Woodland distance X
Mean patch 500 m X
Mean patch 1 km X
Total patches 500 m X
Total patches 1 km X
Edge density 500 m X
Edge density 1 km X
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Kestrels, assuming a binary distribution. We included site as a random effect to
account for potential correlation between nest boxes within sites, but this term was
estimated at zero and subsequently dropped from the models. We did not model
nest success with exposure-based models, given our small sample sizes and given
that studies of nest box use by Southeastern American Kestrels typically treat nest
success as a binary variable. We calculated Akaike information criterion (AICc)
weights (Burnham and Anderson 2002) to determine the best fit model for each
response variable.
Results
Eighteen (31%) of the 58 monitored nest boxes were occupied by nesting Southeastern
American Kestrels. Clutch size was 4.4 ± 0.8 (mean ± SD). Eleven (65%) of
17 nesting attempts were successful, yielding 2.2 ± 1.8 (mean ± SD) fledglings per
nest and 3.4 ± 1.0 fledglings per successful nest. We were unable to determine the
fate of 1 nest. We performed logistic regression analysis on data from the locations
of 51 nest-boxes. Logistical constraints precluded collection of habitat-patch data
at 5 nest boxes in Camp Blanding and 2 nest boxes at Ordway–Swi sher.
We identified the reduced habitat-patch model (percent grass cover) as the best fit
for predicting occupancy by Southeastern American Kestrels (Table 2), but none of
Table 3. Model comparisons, logistic regression of habitat and nest success of Southeastern American
Kestrels, northcentral Florida.
Competing models -2LL k AIC Delta Weight
Null 22.07 1 24.34 0.00 0.29
Habitat amount 500 m 19.82 2 24.68 0.34 0.25
Habitat amount 1 km 19.85 2 24.71 0.37 0.24
Reduced habitat-patch structure 21.60 2 26.46 2.12 0.10
Distance 21.92 2 26.78 2.44 0.09
Habitat pattern 1 km 18.96 4 30.30 5.96 0.01
Habitat pattern 500 m 20.30 4 31.63 7.29 0.00
Habitat patch structure 12.59 10 69.25 44.91 0.00
Table 2. Model comparisons, logistic regression of habitat and occupancy of nest boxes by Southeastern
American Kestrels, northcentral Florida.
Competing models -2LL k AIC Delta Weight
Percent grass cover 54.73 2 58.98 0.00 0.95
Null 64.92 1 67.01 8.03 0.02
Habitat amount 1 km 63.26 2 67.51 8.53 0.01
Habitat amount 500 m 63.52 2 67.77 8.79 0.01
Distance 64.71 2 68.96 9.98 0.01
Habitat pattern 500 m 61.39 4 70.26 11.28 0.00
Habitat pattern 1 km 63.76 4 72.63 13.65 0.00
Habitat patch structure 47.43 10 72.93 13.95 0.00
Habitat patch structure 12.59 10 69.25 44.91 0.00
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the habitat models predicted nest success for Southeastern American Kestrels better
than the null model (Table 3). For both occupancy and success, it also appeared that
habitat-amount models outperformed habitat-pattern models. Regardless, none of the
territory or landscape-scale models had much predictive value.
There was a significant effect of grass cover on occupancy by Southeastern
American Kestrels (F1,49 = 8.04, P < 0.007; Table 2). Nest boxes located in habitats
with a ground cover dominated by grasses (versus shrubs or leaf litter) were more
likely to be occupied (Fig. 1). The canopy closure and bare ground variables were
highly correlated with grass cover (and thus not included in the habitat-patch structure
model; see Methods). Nest boxes occupied by Southeastern American Kestrels
were located in patches that had more grass cover (52 ± 22% [mean ± SD]), relatively
little bare ground (31 ± 15%), and a more open tree canopy (21 ± 18%) than
did unoccupied nest sites, which had less grass (32 ± 18%), more bare ground (42
± 20 %), and a denser tree canopy (35 ± 12%).
Although pine basal area, an important metric widely used by managers in forest
assessment, was typically lower around occupied nest boxes (8.9 ± 9.2 m²/ha
[38.8 ± 40.3 ft²/acre]) than around nest boxes that were unoccupied (13.4 ± 13.5
m²/ha [58.3 ± 58.7 ft²/acre]), it was not significant in the habitat-patch model for
occupancy of nest boxes (F1,41 = 1.82, P = 0.185).
Figure 1. Influence of grass cover (%) on occupancy of nest boxes by Southeastern American
Kestrels in north-central Florida. Dashed lines indicate 95% confidence intervals around
predicted values.
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Discussion
Previous work on habitat relationships for the Southeastern American Kestrel
has been limited to anthropogenic landscapes (e.g., pastures and other agricultural
habitats; Smallwood and Collopy 2009). Our study is the first quantitative assessment
of habitat relationships for this imperiled subspecies using nest boxes in native
plant communities. Breeding Southeastern American Kestrels selected nest boxes in
habitat patches with an average of 52% grass and rarely used areas with less than 25% grass
(Fig. 1). Similar degrees of grass cover were documented in studies of foraging
habitat used by wintering American Kestrels in pastures, citrus groves, and scrub in
southern Florida (Smallwood 1987) and of breeding habitat used by resident Southeastern
American Kestrels in pastures in northern Florida (Smallwood and Collopy
2009). American Kestrels in Florida appear to choose areas with similar habitat
structure, regardless of habitat type, location, season, or their migratory status.
Habitat characteristics within nest box territories occupied by Southeastern
American Kestrels (i.e., open tree canopy with few woody shrubs and a graminoiddominated,
low ground cover) were consistent with ecological reference conditions
for sandhills (FNAI 2010). These habitat structural features can be created and
maintained through hardwood-removal programs designed to benefit Picoides borealis
Vieillot (Red-cockaded Woodpecker) and other birds associated with pine
ecosystems with a grass–forb herbaceous layer (Conner et al. 2002, Provencher et
al. 2002). Similarly, populations of fire-dependent bird species such as Peucaea
aestivalis (Lichtenstein) (Bachman’s Sparrow) and Colinus virginianus (L.) (Northern
Bobwhite) increase after restoration of fire to southern pin e ecosystems, likely
because of increases in grasses in the herbaceous layer and arthropods associated
with those grasses (Wilson et al. 1995). We believe managers tracking the impact of
habitat restoration on avian communities in Longleaf Pine sandhills can regard the
presence of Southeastern American Kestrels as a positive indicator of success.
Our findings are consistent with previous evidence that American Kestrels respond
to structural features of open habitat that facilitate hunting on the ground
from perches (Smallwood and Bird 2002). Occurrence and/or nest success of
Southeastern American Kestrels were inversely correlated with the extent of woody
vegetation in pasture-dominated landscapes in New Jersey (Smallwood and Wargo
1997) and Pennsylvania (Rohrbaugh and Yahner 1997) and with proximity to
closed canopy forest in West Virginia and Pennsylvania (Wilmers 1983) and Missouri
(Toland and Elder 1987).
In our study, vegetation structure within the habitat patch used by Southeastern
American Kestrels was more important than the size or shape of the habitat patch or
how the patch fit within the larger landscape, including its proximity to dense forest.
Given that the Southeastern American Kestrel is non-migratory and that natal
dispersal distances in Florida averaged only 4.9 km (Miller and Smallwood 1997),
it is possible that habitat isolation may influence its distribution at a larger landscape
scale that we did not measure. However, when measured at such a scale (4.9-kmradius
circles), neither habitat extent nor habitat fragmentation influenced occupancy
of nest boxes by Southeastern American Kestrels in pasture-dominated landscapes
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in Florida (Brown et al. 2014). In contrast, the size of contiguous habitat patches was
strongly correlated with occupancy of nest boxes by Southeastern American Kestrels
in agricultural landscapes in New Jersey (Smallwood et al. 2009b).
The proportion of successful nesting attempts (65%) was typical of the 67% success
rate reported by Smallwood and Bird (2002) in their review of kestrels across
North America. Our data provide limited support for the influence of habitat on nest
success, but given our small sample size and the fact that “habitat amount” nearly
overcame the null model (Table 3), further research on the minimum habitat patch
size needed for the subspecies seems warranted.
Our results can help managers in peninsular Florida in the placement of nest
boxes to maximize their potential of being used by Southeastern American Kestrels.
Based on our data in sandhills and research on the species elsewhere, we recommend
siting nest boxes in locations with open tree canopies (ideally ≤25% closure) and
low ground cover (≤25 cm; see Smallwood 1987) dominated by grasses. Predictive
habitat modeling for Southeastern American Kestrels using regional land-cover
data (e.g., Cox et al. 1994, Endries et al. 2009) can identify potential habitat for
Southeastern American Kestrels only at a coarse scale, given that the condition and
composition of ground cover within GIS land-cover categories are unknown.
The loss of suitable, open-ground foraging habitat has received little attention in
regional or continental efforts to arrest population declines of the American Kestrel.
Population declines frequently have been attributed to nest-site limitation (i.e., a
lack of suitable tree cavities) in the southeastern US (Hoffman and Collopy 1988,
Smallwood and Collopy 2009) and, at the continental scale, to factors outside the
breeding range (McClure et al. 2017, Smallwood et al. 2009a). However, hypotheses
about the breeding range rarely include consideration of the status and quality
of ground cover. We recommend that further attention be given to maintaining or
reestablishing suitable ground cover in native pyrogenic plant communities for
Southeastern American Kestrels. Additional research may be needed to elucidate
relationships among fire frequency, fire seasonality, fire intensity, and ground cover
suitability for Southeastern American Kestrels in sandhills, scrub, and grasslands
in Florida and neighboring states.
Acknowledgments
We thank the many cooperators and colleagues who helped build, repair, install, or
monitor nest boxes, including S. Earl, J. Garrison, A. Hallman, R. Melvin, G. Morgan, and
D. Pearson. We are grateful to our colleagues J. Brown, N. Klaus, and J. Smallwood for discussions
that helped make this work better. Earlier drafts of the manuscript were improved
by the comments of R. Bielefeld, A. Cox, B. Crowder, and 2 anonymous reviewers. Funding
for this work was provided by Florida’s Nongame Wildlife Trust Fund, Florida’s State
Wildlife Grants program, and in-kind contributions from partners and volunteers.
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