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Site Occupancy and Density of Marsh Birds in Coastal and Freshwater Habitats of Florida
Carolyn M. Enloe, James A. Rodgers, Richard A. Kiltie, and Ryan Butry

Southeastern Naturalist, Volume 16, Issue 3 (2017): 477–487

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Southeastern Naturalist 477 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 Southeastern Naturalist C.M. Enloe, J.A. Rodgers, R.A. Kiltie, and R. Butryn 2017 Vol. 16, No. 3 478 [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 Southeastern Naturalist 479 C.M. Enloe, J.A. Rodgers, R.A. Kiltie, and R. Butryn 2017 Vol. 16, No. 3 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. Southeastern Naturalist C.M. Enloe, J.A. Rodgers, R.A. Kiltie, and R. Butryn 2017 Vol. 16, No. 3 480 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 Southeastern Naturalist 481 C.M. Enloe, J.A. Rodgers, R.A. Kiltie, and R. Butryn 2017 Vol. 16, No. 3 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 Southeastern Naturalist C.M. Enloe, J.A. Rodgers, R.A. Kiltie, and R. Butryn 2017 Vol. 16, No. 3 482 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), Southeastern Naturalist 483 C.M. Enloe, J.A. Rodgers, R.A. Kiltie, and R. Butryn 2017 Vol. 16, No. 3 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). Southeastern Naturalist C.M. Enloe, J.A. Rodgers, R.A. Kiltie, and R. Butryn 2017 Vol. 16, No. 3 484 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 Southeastern Naturalist 485 C.M. Enloe, J.A. Rodgers, R.A. Kiltie, and R. Butryn 2017 Vol. 16, No. 3 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. 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