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C.M. Enloe, J.A. Rodgers, R.A. Kiltie, and R. Butryn
22001177 SOUTHEASTERN NATURALIST 1V6o(3l.) :1467,7 N–4o8. 73
Site Occupancy and Density of Marsh Birds in Coastal and
Freshwater Habitats of Florida
Carolyn M. Enloe1,*, James A. Rodgers1, Richard A. Kiltie1, and Ryan Butryn1
Abstract - Marsh habitats have declined in quantity and quality in Florida, but little quantitative
information exists on the population status of avian species residing in these habitats.
We estimated the occurrence and density of secretive marsh birds in fresh- and saltwater
marshes across Florida during 2011–2012. We detected 11 species; Rallus longirostris
(Clapper Rail) and Gallinule chloropus (Common Moorhen) were the most frequently
detected species. Occupancy rates at freshwater sites ranged from 19 to 64%, with Common
Moorhen the most frequently detected species. Rates at estuarine sites ranged from 2
to 92%, with Clapper Rail the most frequently detected species. Only the Clapper Rail and
Common Moorhen had density estimates ≥1.50 birds/ha; densities varied from 0.06 to 2.20
birds/ha in freshwater marshes and from 0.05 to 2.10 birds/ha in salt marshes. These data
improve knowledge of secretive marsh-bird distributions in Florida.
Introduction
Freshwater marsh and saltmarsh habitats have been deemed in poor and declining
status, respectively, by the Florida Wildlife Legacy Initiative. They are threatened
by altered hydrologic and fire regimes, degradation of water quality, impacts
of nutrient runoff, and sedimentation (FWC 2003). Little undeveloped shoreline
remains in many coastal Florida counties, and even in the least populated counties
only about 60% of the coastline remains undeveloped (Johnson and Barbour
1990). Oil and gas exploration, coastal development, sea-level rise, and disturbance
from incompatible recreational activities, such as off-road vehicle use, continue to
degrade marsh habitats. Despite the continued loss and degradation of marsh habitat,
there is little quantitative information on the population trend and population
status of marsh-dependent avian species (herein defined as Ixobrychus exilis [Least
Bittern], rails, Gallinule chloropus [Common Moorhen], and Aramus guarauna
1Florida Fish and Wildlife Conservation Commission, 1105 Southwest Williston Road,
Gainesville, FL 32601. *Corresponding author - carolyn.enloe@myfwc.com.
Manuscript Editor: Barry Grand
Table 1. List of focal species and their status in Florida (Florida Fish and Wildlife Conservation Commission.
2012). Current ranking score based on Millsap et al. (1990).
Scientific name Common name Status Trend
Ixobrychus exilis Gmelin Least Bittern SGCN Unknown
Laterallus jamaicensis Gmelin Black Rail SGCN Unknown
Rallus longirostris Boddaert Clapper Rail SGCN Unknown
Rallus elegans Audubon King Rail SGCN Unknown
Gallinule chloropus L. Common Moorhen Not listed Unknown
Porphyrula martinica L. Purple Gallinule SGCN Unknown
Aramus guarauna L. Limpkin SGCN Unknown
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[Limpkin]; Table 1). Many species or races of marsh birds are listed regionally
or continentally as species of conservation concern, including subspecies of Rallus
longirostris (Clapper Rail), Common Moorhen, Laterallus jamaicensis (Black
Rail), and Rallus elegans (King Rail), due to steady declines in their populations,
especially in the midwestern and southeastern US (North American Bird Conservation
Initiative 2009). The Florida Wildlife Legacy Initiative, a program of the
Florida Fish and Wildlife Conservation Commission (FWC), lists many of these
marsh birds as species of greatest conservation need (SGCN; FWC 2012:table 3B).
Marsh birds scored high in the FWC’s prioritized vertebrate-conservation protocol
based on biological vulnerability, extent of knowledge of population status, and
management needs (Millsap et al. 1990). In addition, the FWC’s Terrestrial Habitat
Conservation and Restoration Section in October 2009 identified the development
of a survey protocol and determination of the status of the several marsh bird species
as one of its top–priority information needs.
As part of a North American breeding marsh-bird monitoring effort in the 2000s,
the US Fish and Wildlife Service (USFWS) solicited the participation of state agencies
in a nationwide survey directed toward these rarely monitored marsh birds. The
objective of this study was to use and evaluate a standardized nationwide survey protocol
in Florida and estimate the occurrence and density of focal marsh-bird species
in freshwater and estuarine habitats in Florida. Data from this study will be used by
state and federal agencies to establish marsh-bird monitoring strategies. Information
from this study should also provide a starting point for long term trend data used in
establishing management objectives for marsh bird occurrence and abundance.
Field-site Description
We conducted our study in coastal and interior wetlands across Florida (Fig. 1).
Survey sites were located on public and private lands. Salt marshes were each
dominated by one of the following species: Sporobolus alterniflorus (Loisel.)
P.M. Peterson and Saarela (Smooth Cordgrass), Sporobolus bakeri (Merrill) P.M.
Peterson & Saarela (Sand Cordgrass), Sporobolus pumilus (Roth) P.M. Pearson &
Saarela (Saltmeadow Cordgrass), and Juncus roemerianus Scheele (Black Needlerush).
Freshwater marsh vegetation varied between sites and was dominated by
herbaceous grasses, sedges, broad-leaved monocots, and floating- leaved aquatics.
Methods
Site selection
In this study, point count refers to the method of surveying, count point refers
to an individual (secondary) survey location, station point refers to the center of a
count point, and site refers to a primary sampling unit (PSU) of marsh habitat containing
a cluster of count points. We chose random survey sites throughout Florida
in both freshwater and marine–estuarine wetlands.
As a first step in locating count points, the USFWS overlaid a grid of 40-km2
hexagons (each a PSU) on the entire state. A total of 4273 PSUs were placed in
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Florida, of which 192 contained no marsh wetlands, 238 contained estuarine marsh,
3310 contained freshwater marsh, and 533 contained mixed estuarine–freshwater
marsh. We used data from several databases (National Wetlands Inventory,
National Hydrography Dataset, and National Land Cover Database) to conduct a
second-stage selection process to identify all types of marshes within each PSU,
and randomly located 20 points (secondary sampling units, or SSUs) within them.
These 20 points marked the station point of potential SSUs in marshes selected by a
randomized, spatially balanced procedure such as generalized random-tessellation
sampling (Stevens and Olsen 2004) or Hawth’s analysis tools (Beyer 2004). In
response to the Deep Water Horizon oil spill in 2010, 5 tracts of estuarine marsh
habitat in the panhandle were extracted from the Florida 2003 Vegetation and Land
Cover grid using GIS landscape information from the Closing the Gaps Program
(Kautz et al. 2007). We conducted ground-truthing from January through March in
2010 and 2011 to evaluate each potential SSU for accessibility and suitability based
on whether it was flooded, ≥1 ha in size, allowed for all count points to be ≥500 m
from the nearest station point, and had acceptable levels of background disturbance
Figure 1. Marsh-bird survey sites (PSUs) visited during 2010 and 2011. Each site consisted
of 4–6 count points (SSUs). The lack of estuarine survey sites in the southern half of Florida
is due to the absence of large tracts of estuarine marsh habitat there.
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and noise. We excluded areas of deep, open water ≥200 m from the shoreline or
edge. We chose for inclusion PSUs that contained at least 4 count points. We employed
a WAAS-enabled GPS unit to mark each station point. We sampled 31 PSUs
in 2010 and added 8 more PSUs in 201 1.
Marsh-bird surveys
Two-person survey teams—a recorder and an observer—conducted our surveys
from 1 March through 15 June in 2010 and 2011. Our survey protocol followed a
standard procedure described by Conway and Timmermans (2005) and Johnson
et al. (2009). We performed all surveys between 0.5 h before sunrise and 4 h after
sunrise or during the 4 h before sunset. We recorded air temperature, weather, noise
level, and wind speed on the Beaufort scale. Surveyors initiated the survey protocol
upon arrival at each station point unless travel was by motorized boat, in which case
we waited 3 min (i.e., settling period for birds to resume normal behavior) before
beginning the survey. The survey protocol consisted of a 5-min passive-listening
period (recorded as 5 separate 1-minute intervals) followed by a call-broadcast
period. We played recorded calls of focal species (Table 1) to elicit responses (see
Conway and Gibbs 2005, Keller 1977). We played a call for each species for 30 seconds;
each species’ call was followed by 30 seconds of silence before we broadcast
the call of the next species. Thus, with 7 species in the survey, the length of a survey
was 12 min, during which we recorded all birds heard or seen from each station
point. We used a laser rangefinder with an accuracy of 1 m to measure the distance
to each sighted bird or the estimated location of each calling bird and spot-mapped
detected birds to reduce the probability of counting an individual more than once.
We surveyed each count point on 3 occasions (replicates) to enable us to calculate
an estimation of occupancy-detection probability, with 7–10 days between replicates
to lessen the effect of any temporal differences in breeding that may exist
across the state.
Statistical analysis
We applied 2 population modeling approaches for marsh bird species for
which sufficient data were available: occupancy-rate estimation for count points
(Hines 2006, MacKenzie et al. 2006) and population-density estimation based on
individual detection distance (Buckland et al. 2001). We employed the package
UNMARKED, version 0.9-8 for the R statistical environment (R Development
Core Team 2012) to conduct occupancy analysis (Fiske and Chandler 2011, Fiske
et al. 2012). Density estimation was performed with the program DISTANCE 6.0,
release 2 (Thomas et al. 2010a, b).
Occupancy models and distance-based density models treat detection probability
differently; thus, we included in our occupancy modeling only detections
recorded within 100 m of the station, whereas we included detections as far as
250 m from the station point in density analyses. We assumed there was no net
change in the population at count points during the intervals between the 3 survey
visits made each year. When data were sufficient, we estimated density for both
years combined. For density modeling in DISTANCE 6.0, we treated the 3 visits to
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each count point as point-specific measures of survey effort, which we handled as
multiplier variables.
We did not include spatial or other covariates in the models, but nearly all the
models used provided acceptable fits by parametric bootstrap tests for the occupancy
models (Kéry and Chandler 2012) or by the Cramér von Mises test for the
distance-based models (Burnham et al. 2004). No covariates were involved; thus,
model-selection issues did not arise for the occupancy-modeling analyses. Though
no covariates were involved for distance-based density modeling, model selection
had to be addressed because a variety of key functions and series expansions for the
detection functions was available to fit the data for individual species. We applied
6 combinations of key functions and series expansions in the analysis of each data
set as recommended by Buckland et al. (2001): half normal (HN) with cosine series
(CO), HN with Hermite polynomial (HP), hazard rate (HR) with CO, HR with
simple polynomial (SP), uniform (UN) with CO, and UN with SP. In one case, we
applied HR with HP when none of the other combinations provided adequate fit.
We fixed the number of key function parameters at 1 for HN, 2 for HR, and 0 for
UN, but each series expansion could involve 0–5 parameters. For each combination
of key function and series expansion, models were considered stepwise with
an increasing number of series-expansion parameters, and the number of seriesexpansion
parameters chosen was that which preceded the number entailing an
increase in the Akaike information criterion corrected for small sample size (AICc).
We again compared the 6 key-function models with their chosen series-expansion
formulations again by AICc. If the key function plus series expansion with the lowest
AICc did not achieve at least 90% AICc weight, we applied model averaging
using models from the minimal subset of 6 that constituted ≥90% cumulative AICc
weight. We followed the methods described for density estimates by Burnham and
Anderson (2002) to perform model averaging. Unconditional confidence intervals
for estimates from single-model or model-averaged analyses were based on analytic
conditional variance estimates (Buckland et al. 2001) because bootstrapping
could not be sustained for these data without the occurrence of terminal errors in
the DISTANCE program.
Results
Detections at PSUs
We detected 7 species and 1143 birds (auditorily or visually) among the 31 PSU
sites (55 count points at freshwater sites and 89 count points at estuarine sites) in
2010. The Clapper Rail (844 detections across 90 count points) and the Common
Moorhen (169 detections across 33 count points) were the most frequently detected
species. We detected Black Rails on only 4 occasions, 1 bird each at 4 count points.
The number of birds detected at a PSU ranged from 0 to 257. Surveys at 1 estuarine
and 2 freshwater PSUs resulted in no detections.
We detected 7 species and 1519 birds (auditorily or visually) among the 39
PSU sites (93 count points at freshwater sites and 114 count points at estuarine
sites) in 2011. The Clapper Rail (779 detections across 170 count points), Common
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Moorhen (470 detections across 62 count points), and Limpkin (129 detections
across 21 count points) were the most frequently detected species. Black Rails
were detected on only 4 occasions, 2 birds each at 2 count points. The number of
detections at a PSU ranged from 0 to 127. We detected a maximum of 127 birds at
a freshwater PSU and 112 birds at an estuarine PSU. Surveys at 3 freshwater PSUs
resulted in no bird detections. Among the freshwater PSUs, the Common Moorhen
(469 detections across 62 count points), Limpkin (129 detections across 21 count
points), and Purple Gallinule (60 detections across 21 count points) were the most
frequently detected species. Among estuarine PSUs, the Clapper Rail (771 detections
across 104 points) and Least Bitterns (21 detections across 15 count points)
were the most frequently detected species.
Count-point occupancy rates
We detected 7 species in 2010 and 10 species in 2011. Limpkins, Purple Gallinules,
and Common Moorhens were detected only at freshwater sites, whereas
Clapper Rails were only observed in estuarine areas. Low detection rates disallowed
analyses for Black Rails (Fig. 2). Occupancy-rate estimates varied among
species and between habitats and ranged from 19 to 64%; the Common Moorhen
was the most frequently detected species. Occupancy rates of species at estuarine
sites ranged from 2 to 92%; the Clapper Rail was the most frequently detected
species. For species detected in both habitats, Least Bitterns were detected more
frequently at freshwater count points.
Count-point densities
Density estimates varied among species and between habitats; only those for
Clapper Rails and Common Moorhens were ≥1.50 birds/ha (Fig. 2). Density estimates
at freshwater sites ranged from 0.06 to 2.20 birds/ha; the Common Moorhen
was the densest at those sites. Density estimates at estuarine sites ranged from 0.05
to 2.10 birds/ha; the Clapper Rail was the densest at those sit es.
Discussion
The species that exhibited the greatest estimates of occupancy rate and density
were the Clapper Rail in estuarine marshes and the Common Moorhen in freshwater
marshes. The relatively low occupancy of 23% and density of 0.7 birds/ha
suggest a small population for the King Rail where it was detected. The relatively
low detection rate for the Purple Gallinule was due to its absence from many of our
count-point surveys in northern Florida, though we know it occurs in north Florida
and detected it while ground-truthing sites. The reason for the low detection rate
for Black Rail is unclear, but our survey protocol and sampling design should have
been robust for surveying the species because the playback method is effective
in determining site occupancy for the Black Rail (Richmond et al. 2008), and our
survey period, 15 March–30 May, was within its nesting season (May–August) in
Florida (Legare and Eddleman 2001). Furthermore, many of our estuarine countpoints
contained low vegetation and hypersaline patches of bare sand (i.e., salt
pannes) characteristic of Black Rail habitat in Florida (Legare and Eddleman 2001),
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and we detected birds at some of these sites in subsequent years while researching
other species. Perhaps our low detection rate is an effect of the Black Rail’s low
population size or inadequate coverage of their primary habitat.
Nichols and Williams (2006) identified 2 general types of monitoring: targeted
surveys and surveillance surveys. Targeted monitoring is hypothesis-driven and
Figure 2. (A) Occupancy and (B) density of focal species of marsh birds at all 39 study sites
(PSUs) during 2010–2011. The number of secondary sites, on which the occupancy and
density models were based, was 121 (Clapper Rail, detected in estuarine sites only); 107
(Common Moorhen, Limpkin, and Purple Gallinule, detected in freshwater sites only); or
228 (Black Rail, King Rail, and Least Bittern detected in both kinds of habitats).
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the study design intended for detecting population trends and habitat associations,
which often are responses to specific conservation or management actions. Our survey
is considered surveillance monitoring, which is conducted to collect baseline
information or to determine the status of a species. Results of surveillance monitoring,
however, can also facilitate the development of more-targeted monitoring for
species that are imperiled or rare (e.g., targeted surveys for Black Rails in Florida).
The challenge in designing a survey is to differentiate between a true negative
(i.e., the species is not present) and a false negative (i.e., the survey fails to detect
the species when present) for a species. Surveys and analyses that include estimation
of detection probabilities and detection covariates appear to overcome biases
of earlier survey techniques, but surveys still must meet certain assumptions (Buckland
et al. 2001, 2004; Burnham et al. 2004; Thomas et al. 2010a, b). These methods
require a relatively large number of detections (e.g., 60–100 per species) to be able
to estimate the detection function, which reduces the use of DISTANCE sampling
to all but the most common species (Johnson 2008). Johnson (2008) reviewed
avian survey methods and concluded that the detection of a bird depended on its
availability (i.e., the bird must be present in order to be detected, which depends on
the duration of the survey, time of year, reproductive status, time of day, distance
from observer, weather conditions, and density of birds) and perceptibility (i.e.,
the conditional detection of a bird when it is present, which depends on distance
from observer, attenuation of calls, habitat features, skill of the observer, weather
conditions, detectability of calls, and abundance of birds). Marsh-bird surveys
should follow a well-established protocol and incorporate randomized locations of
count points within the marsh site as we have done here because the probability of
detecting a bird can vary by air temperature, day of the year, region of the marsh
being surveyed, survey location within the marsh (e.g., edge versus interior location),
and vegetation type (Alexander 2011). In addition, care must be taken not to
survey exclusively along easily accessible edges of habitat because the density of
vegetation along an edge and vegetation–water interspersion are the best predictors
of abundance for many marsh-bird species (Lor and Malecki, 2006, Rehm
and Baldassarre 2007). Incorporating the methodologies referenced above with
the National Marsh-Bird Monitoring protocol developed by the USFWS (Conway
and Gibbs 2005, Conway and Timmermans 2005, Johnson et al. 2009) allows for
relatively robust estimates of occurrence and abundance for these secretive species.
In addition, the inclusion of habitat covariates that define marsh structure can allow
inferences about marsh-bird distributions and abundances between and among
habitat types that can be useful for planning wetland habitat management activities
and tracking changes in study locations over time.
Recommendations
We employed the National Marsh-Bird Monitoring protocol with varying degrees
of success. SSUs were selected within each PSU supplied by the USFWS
based on dated (circa 1990s) information from the National Wetlands Inventory. We
found that many of the SSUs were no longer marsh due to development, contained
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little or no emergent vegetation, or were dry due to drought conditions during the
2010–2011 survey periods. Information that is more-current (since 2004) and accurate
(pixel resolution of 30 m or 900 m2) is available from the Florida Vegetation
and Land Cover grid from the Closing the Gaps Program (Kautz et al. 2007) and
should be used to identify marshes for future surveys. We also recommend that detailed
information that defines marsh structure and prey abundance be collected at
count points during future targeted surveys to assist in evaluating distribution and
abundance of focal species.
We detected several species of marsh birds (Black Rail, King Rail) at low frequencies
in this study. When surveying sedentary, rare, or inconspicuous species,
longer count periods can increase the number of detections (Johnson 2008). Line
counts, which are often more efficient than count points and generate smaller error
in estimating the distance to a bird, may result in less bias than do count points
(Johnson 2008). Modifications to the survey protocol should be considered for
optimizing the detection rate of rare species of marsh birds. A single–species survey,
during which the call of only a focal specie is broadcast, may result in more
detections for rare species. Specifically, we recommend reassessing occupancy and
abundance rates of Black Rails in Florida with a survey protocol that restricts the
randomly located SSUs and line transects to the Black Rail’s preferred habitat and
broadcasts only its calls.
Using the data collected during the present study, we were able to calculate
the number of count points and repeated visits needed to attain a certain level of
confidence in the monitoring of species occurrence and relative abundance, which
will be helpful in future surveys. Based on our results, only surveys for the Clapper
Rail and Common Moorhen could be completed with a reasonably low number of
count points and repeated visits. We recommend that statewide surveys of those
species should be repeated every 5 years to provide long-term population-trend
data for their management. Their frequent detection, widespread distribution, and
relatively large occupancy and abundance rates with narrow confidence intervals
should provide robust power for detecting changes in their populations in Florida.
Acknowledgments
We gratefully acknowledge the numerous field technicians and volunteers who assisted
in data collection, exposing themselves to mosquitoes and no-see-ums, and working
during the early morning and late evening hours: Joe Bozzo, Robin Boughton, Stephen
Brooks, Dawn Brumley, Janell Brush, Adam Casavant, Kevin Church, Cameron Carter,
Steve Daniels, Tim Dellinger, Erik Eckles, Marty Folk, Stanley Kirby, Amy Schwarzer,
Steve Schwikert, Oona Takano, Rio Throm, and Susanna Toledo. Primary funding was
via a grant from the USFWS facilitated by Mark Seamans and the FWC’s Nongame Trust
Fund. We also thank the following entitites that allowed us access to lands under their
jurisdiction: Long Point Golf Club, Southwind Homeowners Association, Mennonite
Lakewood Retreat, Guana Wildlife Management Area, US Coast Guard Station Ponce
Inlet, Tomoka State Park, Lower Suwannee National Wildlife Refuge (NWR), St. Vincent
NWR, St. Marks NWR, and Chassahowitzka NWR. Andrew Cox, Bland Crowder, Karl
Miller, and Tim O’Meara provided excellent reviews of earlier drafts.
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