Informing conservation planning with population estimates and divergent landscape associations of tidal marsh birds

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Jones, Raymond B. Iglay, Kristine O. Evans, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6214450/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 16 Oct, 2025 Read the published version in Wetlands → Version 1 posted 5 You are reading this latest preprint version Abstract Tidal marsh birds across the northern Gulf of Mexico are subject to numerous natural and anthropogenic disturbances. Despite many species of concern inhabiting the area, baseline population estimates are lacking which prohibit effective tidal marsh conservation planning. Thus, we generated population estimates and determined landscape associations at local and broad scales for Clapper Rails ( Rallus crepitans ), gallinules ( Gallinula galeata and Porphyrio martinicus ), Least Bitterns ( Ixobrychus exilis ), Marsh Wrens ( Cistothorus palustris ), Seaside Sparrows ( Ammospiza maritimus ), Red-winged Blackbirds ( Agelaius phoeniceus ), and Common Yellowthroats ( Geothlypis trichas ) across the Mississippi Gulf Coast. The abundance of most tidal marsh birds in our study was inversely related to landscape heterogeneity, vegetation height, and further proximity to open water. Common Yellowthroat abundance was positively influenced by proximity to uplands and palustrine wetlands, but Seaside Sparrows were negatively impacted. Seaside Sparrow and Clapper Rail abundance was positively associated with proximity to estuarine marsh and percent salt-tolerant vegetation, unlike Common Yellowthroats and gallinules with contrasting relationships. Managers should consider these species-specific differences, as population sizes and landscape relationships varied within the tidal marsh community. Overall, large expanses of tidal marsh across the salinity gradient will promote greater abundance for the tidal marsh bird community as a whole, but local vegetation structure, fine-scale salinity data, and adjacent land cover types will need to be considered for specialist species like Seaside Sparrows. Our robust monitoring and modelling framework can be easily expanded across the Gulf of Mexico, and we recommend this idea be considered by the conservation community across the region. breeding birds Gulf of Mexico landscape associations monitoring multi-scale population estimates tidal marsh Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 INTRODUCTION Tidal marsh ecosystems provide resources for many bird species, several of which are endemic to these geographically limited environments. Organisms inhabiting tidal marshes are regularly subjected to harsh environmental conditions linked to salinity, flooding, storm surge, and anthropogenic disturbances (Greenberg 2006 ). Tidal marshes are highly productive due to rapid nutrient turnover rates, year-round production, and tidal energy flux (Schelske and Odum 1961 ; Ibáñez et al. 2013 ). They also provide refuge from open water, supplying protection and resources for a range of resident and migratory wildlife species. Geomorphic, chemical, and physical processes such as macronutrient cycles, coastal erosion, elevation change, water circulation, and stratification of salinity and sediments all affect wetland structure and productivity, creating a variable and complex coastal environment (Bianchi 2013 ; Snedden et al. 2013 ). The northern Gulf of Mexico region consists of a large complex of tidal marsh ecosystem that spans the interface of open water and land (Battaglia et al. 2012 ). Although species diversity is relatively low, zonation patterns among vegetative communities create a heterogeneous landscape driven by local salinity and elevation gradients (Eleuterius 1972 ). These distinct vegetation zonation patterns occur across local and broad scales and influence the presence of birds and other wildlife by creating a diversity of foraging areas. Tidal marshes along the northern Gulf of Mexico are further impacted by anthropogenic stressors such oil spills, hurricanes, land subsidence, and high rates of relative sea-level rise (Pendleton et al. 2010 ). Tidal marsh birds are important bio indicators of the health of threatened coastal wetlands (Novak et al. 2006 ). However, there is a general lack of knowledge of tidal marsh bird populations globally due to their low detection rates and lack of reliable estimates, especially for populations across the northern Gulf of Mexico (Woodrey et al. 2012 ). Establishing population estimates for conservation priority species is essential to understanding their current and future states and the factors influencing their persistence (USFWS and USGS 2006). Effective management of conservation lands is predicated on having robust population estimates and an understanding of species-specific landscape preferences across spatial scales (Norris 2004 ; Johnston et al. 2015 ). Population estimates are not only critical to management decisions, but they can improve long-term monitoring efforts within an adaptive framework (Lindenmayer and Likens 2009 ). Additionally, spatially-explicit predictions based on associations with landscape features are crucial for conservation planning for sensitive species in disturbance-prone ecosystems, such as tidal marsh birds. Though difficult to determine, baseline population data for tidal marsh birds is greatly needed to be able to assess the impact of future perturbations and the effectiveness of restoration management in the northern Gulf of Mexico (NASEM 2017). The BP Deepwater Horizon Oil Spill in 2010 greatly impacted the Gulf of Mexico and coastal wetlands, making it one of the worst environmental disasters known to the United States (National Commission 2011). Haney et al. ( 2014 ) estimated that ~ 700,000 coastal birds died due to acute oil exposure across the northern Gulf of Mexico, but the population-level consequences for birds of conservation concern have yet to be determined. Following the BP Deepwater Horizon Oil Spill, the Gulf of Mexico Avian Monitoring Network (GoMAMN) was formed to maximize the usefulness of bird monitoring data for conservation and established long-term monitoring objectives to better understand coast-wide impacts, such as the Deepwater Horizon Oil Spill, on Gulf Coast bird populations, including secretive marsh birds (Woodrey et al. 2019 ). The GoMAMN recognized that standardized long-term monitoring is necessary to support bird conservation and restoration actions across the region. Thus, Woodrey et al. ( 2019 ) recommended generating baseline population estimates for tidal marsh birds across the Gulf to assess restoration effectiveness over time and understand basic ecological processes within the tidal marsh community. Most of the current knowledge about tidal marsh bird populations is from the Northeast and mid-Atlantic US coast (Saltmarsh Habitat and Avian Research Program [SHARP] 2022). However, Atlantic coast tidal marshes are generally smaller in area and differ in vegetative structure and tidal influence. Tidal marsh along the Atlantic coast is dominated by smooth cordgrass ( Spartina alterniflora ; Wigand and Roman 2012 ), which is not as abundant along the northern Gulf Coast, often only found in narrow zones near the water’s edge (Eleuterius 1972 ). Northern Gulf Coast marshes are irregularly flooded micro-tidal systems dominated by black needlerush ( Juncus roemerianus ) and some areas receive frequent to intermittent freshwater input from major riverine systems (Stout 1984 ). These differences result in dissimilar vegetation and marsh bird assemblages between the two regions. It is generally understood that tidal marsh birds vary in abundance with salinity, vegetation, and marine/freshwater influence across their range. However, little is known about how local plant zonation and landscape variability affects marsh bird populations along the northern Gulf Coast. Therefore, the objectives of this study were to generate robust estimates of population size for the breeding tidal marsh bird community across the Mississippi Gulf Coast and determine how these birds respond to local and broad scale landscape factors. We hypothesized that tidal marsh birds in our study area would differ in their local and broad landscape associations. In addition, tidal marsh bird species found across the range from salt to fresh marsh (i.e., tidal marsh generalists) would have greater overall abundance. Thus, we predicted that Clapper Rails ( Rallus crepitans ) and Red-winged Blackbirds ( Agelaius phoeniceus ) would have the greatest population size, but more restricted species such as Common Gallinules ( Gallinula galeata ), Purple Gallinules ( Porphyrio martinicus ), Least Bitterns ( Ixobrychus exilis ), Marsh Wrens ( Cistothorus palustris ), Seaside Sparrows ( Ammospiza maritimus ), and Common Yellowthroats ( Geothlypis trichas ) would be less abundant. METHODS Study Area Our study area included 11 tidal marsh complexes across the Mississippi Gulf Coast (Fig. 1 ; Supplementary Material Table S1 ). These marsh complexes were a combination of marine-influenced and riverine-dominated marsh types and generally consisted of fresh (< 3 ppt salinity), intermediate (2–8 ppt salinity), brackish (4–10 ppt salinty), and salt (up to 29 ppt salinity) marsh zones (Eleuterius 1972 ). Elevation and inundation frequency contributed to tidal marsh plant zonation in these Mississippi marsh complexes. Riverine-dominated marshes generally supported a more diverse vegetative community due to the influx of freshwater and reduction of tidal influence (Odum 1988 ). The coastal region of Mississippi was composed of four major drainage systems of the Pearl, Pascagoula, Wolf and Jourdan River basins. However, some areas along the northern Gulf of Mexico received no major freshwater inflow and were mainly composed of brackish and salt marsh (e.g., Grand Bay National Estuarine Research Reserve [NERR]; Wieland 2007 ). Sampling Design We used a spatially-balanced, probabilistic two-stage cluster sampling design to determine sampling locations (Johnson et al. 2009 ). This approach has been successfully used for marsh bird monitoring along the Northeast US coast (Wiest et al. 2016 ) and was the basis for a winter-focused marsh bird population study across Mississippi tidal marshes (Weitzel et al. 2021 ). Our sampling universe for coastal Mississippi consisted of estuarine emergent and estuarine scrub-shrub wetland land cover classes (hereafter estuarine marsh) from the 2010 30 m resolution Coastal Change Analysis Program (C-CAP; National Oceanic and Atmospheric Administration [NOAA] 2010). We used a Generalized Random Tessellation Stratification (GRTS) method to randomly select primary (PSUs) and secondary sampling units (SSUs) within a grid of 40 km 2 hexagons (i.e., North American continental hexagon grid; White 2007 ; Supplementary Material Figure S1 ). We established the PSU sampling frame by a priori identifying hexagons containing at least 10 ha of estuarine marsh resulting in a sampling frame of 52 PSU hexagons. Of these, we randomly selected 25 PSU hexagons plus an oversample of 10-hexagons using GRTS. We used the GRTS approach again to generate 30 (20 base + 10 oversample) SSUs within each PSU hexagon, resulting in 870 potential survey points. Prior to our first sampling season, we visited each of the 870 potential surveys points to determine their suitability for inclusion in our sampling framework. Survey points were removed from our sampling frame if they were not located in estuarine marsh, were inaccessible by boat and/or on-foot, were within 400 meters from another survey point, or were located on private land. Based on these criteria, we eliminated 606 survey points, resulting in a spatially-balanced sampling frame of 264 survey points. After our initial review, we discovered another two survey points were inaccessible and removed them ( n = 262 total survey points). Marsh Bird Sampling We conducted call-broadcast point counts at each survey point during the breeding season from April 14 – July 21, 2021, and April 12 - July 21, 2022, visiting each survey point three times annually. Following the Standardized North American Breeding Marsh Bird Protocol (Conway 2011 ), we collected data for focal and non-focal marsh bird species detected at each survey point during a 13 minute survey (i.e., 5 minute passive count and 8 minute call broadcast period). However, we only used data from the passive count for this study because of potential issues with inflated detection probabilities associated with birds being attracted to the sampling point during playback of conspecific calls. Focal species included secretive tidal marsh birds mainly from the family Rallidae , whereas non-focal species included passerine marsh obligate species (Table 1 ). We estimated the distance (m) to each detected bird. Two observers conducted each survey with one observer recording focal species and the second observer recording non-focal species. Surveys began 30 minutes before sunrise and were completed by 4 hours after sunrise. We also recorded wind speed (km/hr) for each survey and did not survey during high winds (> 20 km/hr), sustained rain, or heavy fog. Table 1 Marsh bird species surveyed during point counts were categorized based on specialist/generalist status in the context of restriction to tidal marsh types. The number of individuals ( n ) for each species is the total individual detections over two years, three visits and 262-point count locations across the Mississippi coast. This list follows the phylogenetic order of the American Ornithological Society’s (AOS) Checklist of North and Middle American Birds (Chesser et al. 2024 ). Alpha code Common name Scientific name Focal/non-focal a Class b N PBGR Pied-billed Grebe Podilymbus podiceps Focal 3 0 KIRA King Rail Rallus elegans Focal 1 3 CLRA Clapper Rail Rallus crepitans Focal 2 7,595 COGA Common Gallinule Gallinula galeata Focal 2 288 AMCO American Coot Fulica americana Focal 3 1 PUGA Purple Gallinule Porphyrio martinicus Focal 2 74 BLRA Black Rail Laterallus jamaicensis Focal 1 0 LEBI Least Bittern Ixobrychus exilis Focal 2 532 MAWR Marsh Wren Cistothorus palustris Non-focal 2 382 SESP Seaside Sparrow Ammospiza maritimus Non-focal 1 1,159 RWBL Red-winged Blackbird Agelaius phoeniceus Non-focal 4 10,950 COGR Common Grackle Quiscalus quiscula Non-focal 4 58 BTGR Boat-tailed Grackle Quiscalus major Non-focal 3 672 COYE Common Yellowthroat Geothlypis trichas Non-focal 3 1,493 a Focal = secretive tidal marsh birds, for which call broadcasts were utilized to increase detectability; Non-focal = passerine obligates b Class 1 = tidal marsh specialist restricted to specific type of tidal marsh (e.g., salt, brackish, or fresh); 2 = tidal marsh specialist not restricted to a specific type of tidal marsh; 3 = habitat generalist but restricted to specific type of tidal marsh; 4 = habitat generalist not restricted to tidal marsh Broad Scale Landscape Variable Sampling We derived broad scale landscape variables using NOAA’s C-CAP land cover data (10 m resolution; NOAA 2017). We determined the land cover classes of interest based on known species-specific ecological preferences according to a tidal marsh bird literature review (Supplementary Material Table S2). We calculated the straight-line distances from each survey point to the nearest point of estuarine marsh, open water, upland/developed land, and palustrine wetlands using the Euclidean distance tool in ArcGIS Pro (ESRI 2022 ). Estimating the distance to land cover types assessed association with specific land cover types while accounting for edge effects (Conner et al 2003 ). Because survey points were located within expanses of estuarine marsh (0 m values), we modified the Euclidean distance raster for this land cover type to include values within estuarine marsh as negative distance (m) values to the estuarine marsh edge (May et al. 2008 ; Jones et al. 2022a , b ). This allowed us to better elucidate the relationship between marsh bird abundance and the extent of estuarine marsh. We accounted for variability in the composition of land cover types by calculating an index of heterogeneity for the land cover types of interest within a 200 m buffer around each point (Jones et al. 2022a , b ). We used a 200 m buffer size because it assessed marsh composition within the local point count area and was used in previous Gulf region marsh bird studies (Rush et al. 2009 ; Leggett 2014 ). This index measures land cover evenness based on the Shannon-Weaver index of species diversity and accounts for the relative proportions of land cover types (Pielou 1969 ). We extrapolated this measurement across the entire study area using a moving window analysis in program R (R Core Team 2021 ). Before modeling, we tested for any problematic correlations (coefficient > 0.6) between landscape variables using a Pearson’s correlation matrix with the rcorr function in package Hmisc (Harrell 2023 ) and by comparing variance inflation factors using the corvif function (Zuur et al. 2009 ) in R (R Core Team 2021 ). Local Scale Vegetation Sampling We conducted vegetation surveys at each survey point following a modified version of the SHARP (2015) point-intercept vegetation sampling design. We bisected a 100 m transect centered on each survey point to create a 50 m radius survey circle (Supplementary Material Figure S2). One observer standing at the survey point recorded the plant species observed and visually estimated the percent cover of each species that occupied ≥ 5% of the survey circle. If the entire area was not visible from the center of the 50 m radius circle, the observer walked along the transect to examine the entire plot. To characterize the vegetation structure of the plot, we measured the maximum height (cm) of each plant species that intercepted the 5-cm radius cylinder of a modified 2m tall Robel pole every 10 meters along the 100 m transect (Robel et al. 1970 ). We selected the variables vegetation height, percent black needlerush, percent smooth cordgrass, and percent freshwater vegetation ( Sagittaria lancifolia, Sagittaria latifolia, Pontederia cordata, Cladium mariscus, Thelypteris palustris , and Alternanthea philoxeroides ) for analysis based on their purported biological relevance to marsh bird distributions (Supplementary Material Table S2). Vegetation height (cm) was the average height of all sampled vegetation species along our 100 m transect surveys at each survey point location. Bird Abundance and Population Size We estimated the abundance and population size of Clapper Rails, Common/Purple Gallinules, Least Bitterns, Marsh Wrens, Seaside Sparrows, Red-winged Blackbirds, and Common Yellowthroats across coastal Mississippi. We selected these species for analysis because they had sufficient detections ( n ≥ 60) to generate reliable abundance estimates and represented a broad range of functional traits among the tidal marsh bird community, such as varying body size, habitat preferences, and foraging strategies. We combined detection data for Common and Purple Gallinule (hereafter gallinules) due to having low species-specific detections and similar habitat associations (Bannor and Kiviat 2020 ; West and Hess 2020 ; Supplementary Material Table S2). We used the pcount function (Royle 2004 ) in the package unmarked (Fiske and Chandler 2011 ) in R (R Core Team 2021 ) to generate hierarchical binomial N-mixture models that accounted for imperfect detection. The binomial N-mixture model required count data replicated across space and time and consisted of two sub-models that accounted for state and observation processes. We truncated detections by distance based on distance detection curves generated for each species (Supplementary Material Table S3). We fitted separate hierarchical N-mixture models using our locally-derived or remotely-sensed landscape variables to assess the local and broad scale effects of the landscape on tidal marsh bird abundance. We separated the local and broad scale variables because the broad scale abundance models were used to determine site-level abundance and further extrapolate abundance across the study area to estimate the population size of each species, whereas we used the local scale models to investigate variables that significantly impacted species-specific abundance at a finer scale. We fitted all models with observational covariates to account for variation in detection probability. We also included survey point as a random effect in the abundance sub-model to account for variation in abundance among replicated counts at each survey point. We analyzed abundance by year to avoid violating the model assumption of population closure (Kéry and Royle 2017 ) and to ensure confidence in our population estimates. Although combing years by “stacking” data in static N-mix models seems acceptable in some cases (Kéry and Royle 2021 ), it could lead to understated uncertainty, and we found that stacking did not improve our model fit results. We performed model selection by building model complexity in two stages (observation and abundance sub-models) and selecting the best model using Akaike Information Criterion (AIC). First, we fit the global observation sub-model with the effects of wind (Beaufort scale 0–5), visit (occasion 1–3), date (Julian day), and observer and identified models with ΔAIC ≤ 2. We then chose the best model with the lowest AIC and fewest number of parameters (K). Next, we built upon the resulting model by adding the abundance covariates (local or landscape) and comparing AIC and K again for models with a ΔAIC ≤ 2. If dropping a non-significant variable from the most parsimonious model improved AIC, it was removed. The final best model was identified by comparing AIC and fit between Poisson and Zero-inflated Poisson distributions. We assessed model fit by comparing model residuals using QQ plots with the nmixgof package (Knape et al. 2018 ) in R. The QQ plots were generated for site-sum and observation randomized quantile (rq) residuals. Site-sum rq residuals were computed from aggregating counts across sites. Whereas, we derived the observation rq residuals from the observation sub-model. We evaluated model overdispersion, the presence of higher variance in the observed data than the expected variance of the model, by calculating an overdispersion parameter, the variance inflation factor (c-hat). Finally, using the broad scale models, we determined site-level abundance, or the estimated abundance within point count survey areas across our sites, within a 95% confidence interval using the ranef and predict functions in package unmarked (Fiske and Chandler 2011 ). We determined the total population size for each species by extrapolating the expected site-level abundance estimate across the study area using the function predict in package unmarked (Fiske and Chandler 2011 ). Applying this prediction to every cell in our study area raster produced a spatially-explicit map of abundance values for each species (Supplementary Material Figures S4 – S10). To determine the total abundance for each species across the survey area within a 95% confidence interval, we summed extrapolated abundance values from each raster cell in the predict output in unmarked (Fiske and Chandler 2011 ). The total population size for each study species was calculated within estuarine marsh across coastal Mississippi as classified by NOAA’s C-CAP data. Additionally, we calculated Seaside Sparrow population size within salt marsh using the salt marsh land cover data layer from the U.S. Geological Survey Delineation of Marsh Types (Enwright et al. 2014 ), because Seaside Sparrows are known to be restricted to areas of saline marsh (Greenlaw and Post 2022). RESULTS We detected 22,292 marsh birds over 1,572 surveys across the survey period of April-July, 2021 and 2022 in coastal Mississippi (Table 1 ). The mean number of individual marsh birds per survey point differed among marsh complexes, with the greatest mean detections (across visits and years) at Grand Bay NERR (106 ± 18, n = 2,252). Although the Pascagoula (97 ± 38, n = 8,013) and Hancock County marshes (84 ± 22, n = 3,351) were the largest marsh areas surveyed (Supplementary Material Table S1 ), smaller sites such as Deer Island (83 ± 39, n = 248), Graveline (79 ± 30, n = 1,424), and Gulf Park Estates (73 ± 17, n = 439) had comparable mean marsh bird detections per survey point across years. Tidal Marsh Bird Abundance and Population Estimates Red-winged Blackbirds and Clapper Rails were the most abundant species among our survey sites (Table 2 ). Population size estimates increased for Clapper Rails (17%), gallinules (471%), Least Bitterns (130%), and Red-winged Blackbirds (112%) from 2021 to 2022, but Common Yellowthroat population size decreased by 30%. Interestingly, the total population size increased for Marsh Wrens (56%) and Seaside Sparrows (256%) from 2021 to 2022, but their site-level abundance decreased between survey years (83% and 60%, respectively). Site-level abundance also declined between years for the Common Yellowthroat (91%). QQ plots of randomized-quantile residuals against standardized normal residuals indicated adequate fit for each model and species between years, but there was some deviation from the identity line at large quantile values (Supplementary Material Figure S3). Overdispersion values (c-hat) slightly deviated from one across all models (range = 0.69–1.74), but there was no systematic indication of problematic overdispersion (Table 2 ). Table 2 Site-level abundance and population size model results vary in 2021 and 2022 for Clapper Rails, Common and Purple Gallinules, Least Bitterns, Marsh Wrens, Seaside Sparrows, Red-winged Blackbirds, and Common Yellowthroats. Abundance and population size per year is reported with a 95% confidence interval. The best ranked models for each species are separated into observation and abundance sub-models. The top model per year was chosen from a candidate set of models within 2 ΔAIC units based on the number of parameters (K) and AIC. Each models’ associated c-hat values indicated low amounts of overdispersion. Species Observation sub-model Abundance sub-model Year Data type K C-hat Site-level abundance Total population size CLRA date + wind + visit + observer LH + Watdist + Estdist+(1|Point_ID) 2021 P 8 1.27 6,666 [6,512-6,823] 28,655 [20,558 − 40,319] date + wind + visit + observer LH + Watdist + Estdist + Updist+(1|Point_ID) 2022 P 9 1.74 7,952 [7,801-8,101] 33,510 [24,014–47,847] CPGA* date + wind + visit + observer LH+(1|Point_ID) 2021 P 6 1.11 128 [109–146] 360 [95 − 1,374] date + observer LH+(1|Point_ID) 2022 P 4 0.82 1,099 [1,038 − 1,165] 2,057 [1,032 − 3,431] LEBI date + wind + visit + observer LH+(1|Point_ID) 2021 P 6 0.69 2,487 [2,394-2,578] 14,124 [4,284 − 46,623] date + wind + visit + observer LH + Watdist + Estdist+(1|Point_ID) 2022 P 8 1.09 5,775 [5,426-6,108] 32,539 [9,337 − 114,015] MAWR date + visit + observer Estdist + Watdist+(1|Point_ID) 2021 P 6 1.68 2,555 [2,460-2,650] 6,571 [2,424 − 18,790] date + visit + observer LH + Watdist + Estdist+(1|Point_ID) 2022 P 7 0.86 436 [404–471] 10,282 [3,659 − 29,545] SESP date + wind + observer LH + Estdist + Updist + Paldist+(1|Point_ID) 2021 P 8 0.98 1,528 [1,467-1,590] 14,386 [3,804 − 502,293] date + wind + visit + observer Estdist + Paldist+(1|Point_ID) 2022 ZIP 7 1.10 615 [563–673] 51,249 [9,558 − 479,050] RWBL date + wind + visit + observer LH + Watdist + Estdist+(1|Point_ID) 2021 P 8 0.92 5,568 [5,457-5,679] 79,517 [55,998 − 112,969] date + wind + visit + observer LH + Watdist+(1|Point_ID) 2022 P 7 1.37 11,083 [10,757 − 11,421] 168,196 [128,397 − 220,587] COYE date + wind + visit LH + Paldist + Updist+(1|Point_ID) 2021 ZIP 7 0.85 979 [929-1,032] 8,613 [4,089 − 18,229] date + wind + visit LH + Paldist+(1|Point_ID) 2022 ZIP 6 1.37 592 [547–633] 6,055 [3,783-9,741] LH = landscape heterogeneity, Watdist = distance to open water, Estdist = distance to estuarine marsh, Updist = distance to developed/uplands, Paldist = distance to palustrine wetlands, (1|Point_ID) = random effect of point count survey ID, ZIP = zero-inflated Poisson, P = Poisson, c-hat = overdispersion or variance inflation factor. *CPGA = combination of the COGA and PUGA alpha codes Landscape Associations Broad scale. Landscape heterogeneity, distance to estuarine marsh, and distance to open water were most often negatively associated with tidal marsh bird abundance (Fig. 2 ). Every study species had a negative relationship with increasing landscape heterogeneity (Fig. 2 a). As distance to estuarine marsh increased, marsh bird abundance decreased, except for Common Yellowthroats and gallinules, which were unaffected by distance to estuarine marsh (Fig. 2 b). Similar to distance to estuarine marsh, marsh bird abundance decreased with distance further from open water, except for gallinules, Seaside Sparrows, and Common Yellowthroats (Fig. 2 c). Seaside Sparrow abundance increased with greater distance from palustrine wetlands (Fig. 3 a) and upland/developed lands (Fig. 3 b), whereas Common Yellowthroat abundance decreased for both land cover types (Fig. 3 ). Least Bittern and Marsh Wren landscape associations differed between years. The only landscape variable affecting Least Bittern abundance in the first year of the study was landscape heterogeneity (Table 2 ; Fig. 2 a). Marsh Wrens were negatively impacted by distance to estuarine marsh in the first year but not in the second (Fig. 2 b). In the second year, Marsh Wren abundance became negatively associated with landscape heterogeneity (Fig. 2 a). Local scale. Vegetation height, percent black needlerush, and percent smooth cordgrass were often associated with marsh bird abundance (Figs. 4 and 5 ). Increasing vegetation height negatively impacted Clapper Rail, gallinule, Seaside Sparrow, and Red-winged Blackbird abundance, but it positively impacted Marsh Wren abundance (Fig. 4 ). Clapper Rail, Seaside Sparrow, and Red-winged Blackbird abundance decreased with increasing percentage of freshwater vegetation (Fig. 5 a). Clapper Rail and Seaside Sparrow abundance was positively associated with smooth cordgrass (Fig. 5 b) and black needlerush (Fig. 5 c), whereas gallinules, Red-winged Blackbirds, and Common Yellowthroats were negatively associated with both plant species (Fig. 5 b and 5 c). Marsh Wrens and Least Bitterns were the only species not impacted by black needlerush or smooth cordgrass percent coverages. Our local scale variables had no relationships with Least Bittern abundance. DISCUSSION We generated baseline population estimates for eight breeding tidal marsh bird species that allow for evaluating population change over time in coastal Mississippi with a sampling design that is applicable to other marsh systems along the northern Gulf Coast. Our work demonstrates that broad scale population estimates can be derived using standardized approaches in Gulf of Mexico marshes which are a fundamentally different marsh ecosystem from Northeast US coast tidal marsh systems (Wiest et al. 2016 ). Further, our models captured the diverse marsh systems of Mississippi and provide complementary information regarding plant species composition and zonation at both local and broad spatial scales. We expected and confirmed that Red-winged Blackbirds and Clapper Rails were the most abundant breeding tidal marsh birds across coastal Mississippi, mainly because they are more generalist species, occurring across the range of marsh types (Rush et al. 2020 ; Yasukawa and Searcy 2020 ). Whereas no other abundance estimates for breeding marsh birds are available for the northern Gulf of Mexico, Seaside Sparrows (230,000; 95% CI = 174,000-286,000) were estimated to be the most abundant tidal marsh bird specialist along the northeast Atlantic coast, and Clapper Rails (151,000; 95% CI = 90,000-212,000) were the second most abundant (Wiest et al. 2016 ). The difference in abundance between the two regions is likely due to the greater tidal amplitude in the Northeast which results in a greater proportion of salt marsh in their smaller tidal marsh complexes across the region. Prior to our study, breeding marsh bird population estimates were nonexistent for coastal Mississippi. However, Weitzel et al. ( 2021 ) generated winter population estimates for non-breeding tidal marsh birds across the Mississippi coast using a distance-sampling approach. Of the eight species in our study, Clapper Rails, Marsh Wrens, Seaside Sparrows, Red-winged Blackbirds, and Common Yellowthroats were the only species present across seasons. Clapper Rail and Common Yellowthroat populations were higher during the breeding season, likely a reflection of a decrease in detectability of rails during the non-breeding season when they are less prone to call. For Common Yellowthroats, this lower abundance probably reflects the migratory nature of individuals found along the coast during the breeding season. Marsh Wrens, Seaside Sparrows, and Red-winged Blackbirds were more abundant during the non-breeding season than during the breeding season. This result is consistent with what we know about the life history of these three species being short-distance migrants wintering primarily along the northern coast of the Gulf of Mexico (Hamel 1992 ). Comparisons of abundance are informative, because we understand breeding and nonbreeding populations to be distinct and expect population estimates and landscape relationships to differ among species, seasons, and geographies (Woodrey et al. 2019 ). Therefore, comprehensive management plans will benefit from intra-annual population data for each species. The confidence around our population estimates varied across species likely due to differences in data types and detection probabilities. Seaside Sparrows had extremely broad confidence intervals, leading us to have less confidence in our population estimates for this species. These broad confidence intervals could result from their high detection probability (> 0.50) and semi-colonial behavior during the breeding season (Post 1974 ). A clumped distribution could violate independence assumptions of N-mixture models (Joseph et al. 2009). However, our site-level abundance estimates were less concerning than our broad, coast-wide extrapolations. Seaside Sparrows, as salt marsh specialists, are sensitive to local scale salinity gradients across the landscape, as confirmed by our local scale models. We observed Seaside Sparrows to congregate in higher salinity marsh areas that are not distinguishable in our coast-wide remotely-sensed landscape maps. Unfortunately, C-CAP classifies marsh broadly into areas of estuarine emergent or palustrine wetlands but does not include brackish or salt marsh cover types. In an attempt to account for the lack of fine scale marsh types, we used the salt marsh land cover data layer from the U.S. Geological Survey Delineation of Marsh Types Report (Enwright et al. 2014 ). Although this map had a salt marsh land cover class, it was not fine enough to distinguish zones of smooth cordgrass marsh where we know Seaside Sparrows occur locally. Future efforts should account for non-independent data as best possible and include fine scale land cover classifications when determining regional Seaside Sparrow populations. Several of our study species showed high inter-annual variability in their abundance estimates. Marsh Wren populations are known to vary considerably by year and season across their North American range (Kroodsma and Verner 2020 ), and our results reflect this known annual variation in abundance. In addition, Least Bittern, gallinule, Seaside Sparrow, and Red-winged Blackbird estimates also varied considerably between years. Rush et al. ( 2019 ) found that average densities declined from 2004 to 2015 for Least Bitterns and Common Gallinules in the Mobile-Tensaw River Delta in Alabama and suggest this change over time could be linked to landscape change unaccounted for. Although our broad scale landscape data did not change between survey years, relationships between species abundance and landscape features did change in some cases, which could explain the variation in abundance between years as well as between site-level abundance and total population size. Landscape composition/structure, hydrology, salinity, tidal influence, and resource availability vary across tidal marsh systems annually, and these factors likely contributed to the variability in marsh bird populations as revealed in our study and elsewhere. The observed inter-annual variation in our results highlights the importance of further examining dynamic spatiotemporal factors and conducting long-term monitoring of bird populations (Lindenmayer and Likens 2010 ). Overall, we determined robust population estimates by minimizing error and avoiding model assumption violations through the use of our systematic sampling design, standardized sampling methodology, and a modelling approach that considers imperfect detection. However, we acknowledge that N-mixture models can be sensitive to low detection probabilities and unmodeled heterogeneity (Veech et al. 2016 ; Kéry and Royle 2017 ; Duarte et al. 2018 ). We determined model fit was adequate across our study species and there were no indications of problematic overdispersion in our models. Nevertheless, our results should be interpreted with caution, and future efforts for implementing hierarchical models of abundance should consider model sensitivity. Our results indicated that breeding tidal marsh birds in coastal Mississippi prefer more homogenous land cover composition and closer distances to estuarine marsh with open water nearby. This observed negative relationship with landscape heterogeneity seems to contradict Weitzel et al. ( 2021 ) who found positive associations of non-breeding Clapper Rail and Marsh Wren abundance with increasing vegetation ecotones. Whereas that study was focused on the non-breeding season in coastal Mississippi and examined the local diversity of ecotone types, our study in the breeding season used a heterogeneity index that essentially measured the evenness of broad land cover types according to remotely sensed land cover data (e.g., estuarine wetlands, palustrine wetlands, developed land/uplands, and open water). Additionally, this could be a result of differences among breeding and non-breeding habitat, as the marsh landscape is more robust and varied in the breeding season (Woodrey et al. 2019 ). Previous studies have shown positive relationships between marsh bird abundance and the diversity of vegetation types within inland, palustrine marsh (Alexander and Hepp 2014 ), but there is a lack of research on how land cover heterogeneity affects estuarine marsh bird abundance in terms of the proportion of land cover types. On a broad scale, our study species likely prefer a single marsh community type as opposed to many, because they are estuarine marsh obligates and have been positively associated with larger, contiguous marsh patches in other parts of their range (Benoit and Askins 2002 ; Spautz et al. 2006 ). Given our remotely sensed data only captured broad land cover types, we recommend examining this relationship with finer scale landscape data that better reflects the diversity of land cover types within tidal marsh. Most of our study species were negatively associated with greater distances to estuarine marsh and open water, except for Common Yellowthroats and gallinules. Although present in estuarine marsh, these species prefer lower salinity, palustrine wetlands across their range likely resulting in their abundances being more driven by vegetation structure and density (Bannor and Kiviat 2020 ; Guzy and Ritchison 2020 ; West and Hess 2020 ). For example, Common Yellowthroats use diverse landscape types across their range, from wetlands to dry uplands; however, they generally seem to prefer dense undergrowth and thickets (Guzy and Ritchison 2020 ). While Common Yellowthroat abundance increased with proximity to palustrine wetlands and upland/developed lands, Seaside Sparrows expressed their status as salt-marsh specialists with a sensitivity to coastal development/uplands and palustrine wetlands, which is consistent with previous findings in Mississippi (Rush et al. 2009 ; Leggett 2014 ). Because Clapper Rails, Least Bitterns, and Marsh Wrens use the water’s edge for foraging (Kroodsma and Verner 2020 ; Poole et al. 2020 ; Rush et al. 2020 ), we expected to see a decrease in their abundance as distance to open water increased. The interspersion of open water via tidal creeks likely provides adequate foraging habitat for these species. More research regarding preference for extent and interspersion of open water is warranted for these species to better direct management actions. Our multi-scale landscape data revealed some unexpected but complementary relationships across marsh bird species. For example, our models indicated the broad scale effect of proximity to palustrine wetlands did not influence breeding Clapper Rail abundance, but the local presence of freshwater plants negatively impacted Clapper Rail abundance. Fiddler crab densities vary across marsh vegetation and salinity (Mouton and Felder 1996 ). Therefore, the presence of freshwater plants could mean fewer fiddler crabs, the preferred prey of breeding Clapper Rails in Mississippi (Rush et al. 2010a ). Similarly, broad scale predictors of distance to estuarine or palustrine wetlands had no significant effect on gallinule abundance, but the increasing percentage of smooth cordgrass negatively impacted their populations. In North America, gallinules breed in brackish and palustrine wetlands with emergent and semi-aquatic vegetation (Bannor and Kiviat 2020 ; West and Hess 2020 ) avoiding high salinity wetlands (> 10 ppt). Ultimately, these results reveal the importance of examining landscape variables at both broad and local scales. Vegetation height only had a positive association with Marsh Wrens, whereas all remaining study species exhibited negative associations. Marsh Wrens prefer nest sites in taller vegetation throughout other parts of their range (Kroodsma and Verner 2020 ). The negative effect of increasing vegetation height among our other study species could indicate that the threat of nest flooding may not be as significant across coastal Mississippi, as marsh birds often nest in taller vegetation to avoid flooding (Greenberg et al. 2006 ; Rush et al. 2010b ). However, previous studies showed that Seaside Sparrows chose nest sites with taller vegetation compared to random points at the Grand Bay NERR and Pascagoula River marsh complexes in Mississippi (Lehmicke 2014 ). The negative relationship observed in our study could be an artifact of preference for higher salinity marsh vegetation (e.g., smooth cordgrass) overall, which is sparser and shorter than lower salinity marsh vegetation (Eleuterius 1972 ). Alternatively, marsh birds like Seaside Sparrows and Clapper Rails may not be as impacted by predation pressure due to the concealment of their nests with woven canopies and can afford to nest in shorter vegetation. Last, greater vegetation height and denser structure could decrease detectability due to sound attenuation (Yip et al. 2017 ). Rush et al. ( 2009 ) predicted that an increase in halophytic or salinity tolerant plants may reduce Least Bittern, Marsh Wren, and Common Yellowthroat occupancy while increasing the presence of Clapper Rails and Seaside Sparrows. Our results indicated that Clapper Rail and Seaside Sparrow abundance increased with increasing extent of estuarine marsh and salinity tolerant plants like black needlerush and smooth cordgrass; however, their abundance also declined with upland/developed land encroaching on expansive tidal marshes. Upland and developed land cover may indirectly affect marsh bird abundance due to increased predation and anthropogenic influence, while it may also directly affect it through fragmentation of marsh and by presenting a barrier to movement. Common Yellowthroat, gallinule, and Red-winged Blackbird abundance was negatively impacted by the saline tolerant plant, smooth cordgrass, in our study. Furthermore, Common Yellowthroat abundance increased in proximity to palustrine wetlands. Therefore, encroaching salinity due to sea level rise may negatively impact the future distribution and abundance of Common Yellowthroats, gallinules, and Red-winged blackbirds. Investigating relationships between tidal marsh birds and the landscape of the northern Gulf Coast is of particular importance in the context of increasing pressures on coastal areas. Concomitant anthropogenic, climatic, and other natural disturbances will continue to affect marsh bird population resilience as marshes are converted to open water and saline water encroaches landward (Erwin et al. 2006 ). As marshes slowly migrate landward, terrestrial barriers among topography and developed land will further inhibit marsh movement and connectivity (Enwright et al. 2016 ). This process is broadly referred to as “coastal squeeze” (Doody 2004 ), and it threatens to reduce marsh primary productivity, availability of nesting habitat for breeding birds, and food availability for breeding marsh birds (Hughes 2004 ; Torio and Chmura 2015 ). There is a great need to better understand how the cumulative effects of landscape change will affect marsh bird populations into the future, and special attention is required for our study species, especially gallinules, Least Bitterns, and Marsh Wrens, for which temporal population dynamics and landscape associations remain poorly understood. Management Implications While the primary objective of this research was to generate robust population estimates for breeding tidal marsh birds, these estimates also have implications for refining state and regional monitoring programs such as those priorities identified by the Gulf of Mexico Avian Monitoring Network (Woodrey et al. 2019 ). Here we highlight a few of the more important results from this work that will inform a Gulf of Mexico-wide marsh bird monitoring program. For this study, we calculated samples sizes using sampling point estimation and simulation modelling (Iglay et al. 2020 ). We incorporated both data gleaned from published literature or generated distance analysis statistics using raw data from a limited number of tidal marshes along the Mississippi and Alabama coasts. Given the lack of published data and the patchy geographical representation of our raw data, our confidence in our ability to precisely determine a necessary sample size for the current study was limited. However, with the large number of point count surveys and their broad geographic distribution across the Mississippi coast, we now have the data necessary to develop a more robust sample size estimate. Further, Gulf-wide land cover datasets used in other regions such as the northeastern U.S. are not available for the entire Gulf region. Thus, we had to apply a more non-traditional Gulf-wide land cover classification scheme, namely C-CAP data, for determining our sampling frame. Fortunately, our results indicate that the two-stage sampling design based on C-CAP land cover data can be used to successfully design a sampling framework to generate robust population estimates for breeding marsh birds across the Mississippi coast, and likely expand across the entire Gulf of Mexico region. Our population estimates provide baseline conditions prior to proposed conservation actions or unexpected incidents, such as oil spills. Since we did not have baseline data prior to the Deepwater Horizon Oil Spill, we were not able to confidently assess the impact on tidal marsh birds across the region. However, this study now provides the estimates necessary for quantifying any future breeding tidal marsh bird population-level impacts. Additionally, these estimates improve marsh bird mortality assessments that are inherently difficult to quantify since most tidal marsh birds reside in the marsh interior and are thus underestimated in shoreline mortality surveys (Deepwater Horizon Natural Resource Damage Assessment Trustees 2016 ). Furthermore, these population estimates allow managers to measure the impact of their management activities and identify the importance of maintaining hydrologic conditions and resulting salinity gradients within tidal marshes. Tidal marsh restoration plans should focus on building larger estuarine marsh expanses and protecting the existing expansive, intact marsh complexes. Estuarine marsh with high amounts of open water interspersion meet the needs of both tidal marsh generalists and specialists across the Mississippi coast. When considering local vegetation structure, managers should incorporate shorter vegetation height by planting and protecting expansive short-form smooth cordgrass areas. However, smooth cordgrass monocultures should be avoided at a broad scale as this may not be beneficial for all tidal marsh species, especially Marsh Wrens, Common Yellowthroats, and gallinules. Our results also show that consideration of adjacent land cover types is needed when prioritizing or creating marsh areas. If a salt marsh specialist like the Seaside Sparrow is the target species for restoration/management actions, managers should avoid areas in close proximity to palustrine wetlands, upland/developed lands, and areas with freshwater vegetation locally. If the entire marsh bird community is the target for restoration/management actions, managers will need to consider diverse marsh types and prioritize large areas that include patches of smooth cordgrass marsh greater distances from palustrine and upland/developed land cover. Our study further revealed that we should consider fine-scale landscape and salinity data for salt-marsh specialists like Seaside Sparrows and other marsh structural variables like vegetation density and water levels. Clearly, the often divergent habitat preferences of tidal marsh obligate species should be considered in the context of how to effectively promote marsh bird populations and thus, tidal marsh health. Estuarine tidal marsh restoration projects across the Gulf Coast should focus on monitoring the response of salt and brackish marsh specialists such as the Clapper Rail and Seaside Sparrow. However, freshwater associated tidal marsh species, like gallinules, Least Bitterns, Marsh Wrens, and Common Yellowthroats should be considered for projects focused on providing a broader range of marsh salinity types. Finally, we recommend marsh bird monitoring be expanded to areas of palustrine wetlands across the Mississippi Gulf Coast to better understand the impact of freshwater influence on gallinules, Least Bitterns, Marsh Wrens, and Common Yellowthroats. Robust monitoring of breeding tidal marsh birds can be successfully expanded across the entire Gulf of Mexico Region, and we recommend this idea be discussed, explored, and considered by the conservation community across the region. Declarations Competing Interests The authors have no relevant financial or non-financial interests to disclose. Funding This work was funded with support from the Mississippi Department of Environmental Quality, via the National Fish and Wildlife Foundation Gulf Environmental Benefit Fund [Grant #335414] to Mississippi State University (RBI, KOE, MSW). Additional funding support included National Oceanic and Atmospheric Administration [Awards #NA17NOS4200045, #NA18NOS4200063] to the Mississippi Department of Marine Resources’ Grand Bay National Estuarine Research Reserve (MSW), and the Mississippi Department of Marine Resources [Contract #8200025414] to Mississippi State University (MSW). This publication is a contribution of the Mississippi Agricultural and Forestry Experiment Station, Forest and Wildlife Research Center at Mississippi State University, and based upon work supported by the National Institute of Food and Agriculture, U.S. Department of Agriculture, Hatch project under accession number 7002261 (MSW). Author Contributions MSW, RBI, and KOE developed the study design and supervised the research. JMF assisted with sampling design and methods. LRJ assisted with analytical methods. RVA collected data, conducted the analysis, and wrote the paper. ACKNOWLEDGMENTS We want to thank all of those who assisted in this project and conducted the necessary and often strenuous field work. We also thank personnel from the Saltmarsh Habitat & Avian Research Program (SHARP) who provided advice for implementing a large-scale marsh bird survey. This work was funded with support from the Mississippi Department of Environmental Quality, via the National Fish and Wildlife Foundation Gulf Environmental Benefit Fund [Grant #335414] to Mississippi State University (RBI, KOE, MSW). Additional funding support included National Oceanic and Atmospheric Administration [Awards #NA17NOS4200045, #NA18NOS4200063] to the Mississippi Department of Marine Resources’ Grand Bay National Estuarine Research Reserve (MSW), and the Mississippi Department of Marine Resources [Contract #8200025414] to Mississippi State University (MSW). This publication is a contribution of the Mississippi Agricultural and Forestry Experiment Station, Forest and Wildlife Research Center at Mississippi State University, and based upon work supported by the National Institute of Food and Agriculture, U.S. Department of Agriculture, Hatch project under accession number 7002261 (MSW). Any opinions, findings, conclusions, or recommendations expressed in this publication are those of the author(s) and do not necessarily reflect the view of the U.S. Department of Agriculture. Data Availability Data generated and analyzed during this study are available in Mississippi State University’s Institutional Repository, Scholars Junction, https://scholarsjunction.msstate.edu/td/6312/ . References Alexander BW, Hepp GR (2014) Estimating effects of habitat characteristics on abundances of three species of secretive marsh birds in Central Florida. Waterbirds 37:274–285 Bannor BK, Kiviat E (2020) Common Gallinule (Gallinula galeata), version 1.0. In: Poole AF, Gill FB (eds) Birds of the world. Cornell Lab of Ornithology, Ithaca, NY, USA. https://doi.org/10.2173/bow.comgal1.01 Battaglia LL, Woodrey MS, Peterson MS, et al (2012) Wetlands of the northern Gulf Coast. In Batzer DP, Baldwin AH (eds) Wetland habitats of North America: ecology & conservation issues. University of California Press, Berkeley, CA, USA, pp 75-88 Benoit LK, Askins RA (2002) Relationship between habitat area and the distribution of tidal marsh birds. Wildl Soc Bull 114:314–323 Bianchi, TS (2013) Estuarine Chemistry. In: Day JW Jr, Crump BC, Kemp WM, Yáñez-Arancibia A (eds) Estuarine ecology, Wiley-Blackwell, Hoboken, NJ, USA, pp 39-83 Chesser RT, Billerman SM, Burns KJ, et al (2024) Check-list of North American birds. American Ornithological Society. https://checklist.americanornithology.org/. Accessed January 2025 Conner LM, Smith MD, Burger LW (2003) A comparison of distance-based and classification-based analyses of habitat use. Ecology 84:526–531. https://doi.org/10.1890/0012-9658(2003)084[0526:ACODBA]2.0.CO;2 Conway CJ (2011) Standardized North American marsh bird monitoring protocol. Waterbirds 34:319–346. Deepwater Horizon Natural Resource Damage Assessment Trustees (2016) Deepwater Horizon oil spill: final programmatic damage assessment and restoration plan and final programmatic environmental impact statement. National Oceanic and Atmospheric Administration, Silver Spring, MD, USA Doody JP (2004) ‘Coastal squeeze’—an historical perspective. J Coastal Conserv 10:129–138 Duarte A, Adams MJ, Peterson JT (2018) Fitting N-mixture models to count data with unmodeled heterogeneity: Bias, diagnostics, and alternative approaches. Ecol Modell 374:51–59 Eleuterius LN (1972) The marshes of Mississippi. Castanea 37:153–168 Enwright NM, Hartley SB, Brasher MG, et al (2014) Delineation of marsh types of the Texas coast from Corpus Christi Bay to the Sabine River in 2010. U.S. Geological Survey Scientific Investigations Report 2014-5110, 18 p. scale 1:400,000. http://dx.doi.org/10.3133/sir20145110 Enwright NM, Griffith KT, Osland MJ (2016) Barriers to and opportunities for landward migration of coastal wetlands with sea‐level rise. Front Ecol Environ 14:307–316. https://doi.org/10.1002/fee.1282 Erwin RM, Sanders GM, Prosser DJ, Cahoon DR (2006) High tides and rising seas: potential effects on estuarine waterbirds. Stud Avian Biol 32:214 ESRI (2022) ArcGIS Pro 3.0.3., Environmental Systems Research Institute, Redlands, CA, USA Fiske I, Chandler R (2011) Unmarked: an R package for fitting hierarchical models of wildlife occurrence and abundance. J Stat Softw 43:1–23. Greenberg R (2006) Tidal marshes: home for the few and the highly selected. Stud Avian Biol 32:2 Greenberg R, Elphick C, Nordby JC, et al (2006) Flooding and predation: Trade-offs in the nesting ecology of tidal-marsh sparrows. Stud Avian Biol 32:96 Greenlaw JS, Shriver WG, Post W (2022) Seaside Sparrow (Ammospiza maritima), version 2.0. In: Rodewald PG, Keeney BK (eds) Birds of the world, Cornell Lab of Ornithology, Ithaca, NY, USA. https://doi. org/10.2173/bow.seaspa02 Guzy MJ, Ritchison G (2020) Common Yellowthroat (Geothlypis trichas), version 1.0. In: Rodewald PG (ed) Birds of the world. Cornell Lab of Ornithology, Ithaca, NY, USA. https://doi.org/10.2173/bow.comyel.01 Hamel PB (1992) Land manager’s guide to the birds of the South. The Nature Conservancy, Southeastern Region, Chapel Hill, NC, USA Haney JC, Geiger HJ, Short JW (2014) Bird mortality from the Deepwater Horizon oil spill. II. Carcass sampling and exposure probability in the coastal Gulf of Mexico. Marine Ecology Progress Series 513:239–252 Harrell FE Jr (2023) Hmisc: Harrell, Miscellaneous. R package version 5.0-1 Hughes RG (2004) Climate change and loss of saltmarshes: consequences for birds. Ibis 146:21–28. https://doi.org/10.1111/j.1474-919X.2004.00324.x Ibáñez C, Morris JT, Mendelssohn IA, Day JW Jr (2013) Coastal marshes. In: Day JW Jr, Crump BC, Kemp WM, Yáñez-Arancibia A (eds) Estuarine ecology, Wiley-Blackwell, Hoboken, NJ, USA, pp 1-18 Iglay RB, Woodrey MS, Evans KO, Rush SA, Feura J (2020) Coastal bird stewardship phase 2 - tidal marsh bird research plan, draft 4. Mississippi State University, Starkville, MS, USA Johnson DH, Gibbs JP, Herzog M, et al (2009) A sampling design framework for monitoring secretive marshbirds. Waterbirds 32:203–215 Johnston A, Fink D, Reynolds MD, et al (2015) Abundance models improve spatial and temporal prioritization of conservation resources. Ecol Appl 25:1749–1756. https://doi.org/10.1890/14-1826.1 Jones LR, Johnson SA, Hudson CM, Zollner PA, Swihart RK (2022a) Habitat selection in a recovering bobcat (Lynx rufus) population. PLoS One 17:e0269258 Jones LR, Swihart RK, Gleich DF, Albers G, Johnson SA, Hudson CM, Zollner PA (2022b) Estimating statewide carrying capacity of bobcats (Lynx rufus) using improved maximum clique algorithms. Landscape Ecol 37:2383–2397 Kéry M, Royle JA (2017) Modeling abundance with counts of unmarked individuals in closed populations: binomial N-mixture models. In: Applied hierarchical modeling in ecology: Analysis of distribution, abundance and species richness in R and BUGS: Volume 1: Prelude and static models. Academic Press, London, UK, pp 219-311 Kéry M, Royle JA (2021) Applied hierarchical modeling in ecology: Analysis of distribution, abundance and species richness in R and BUGS: Volume 2: Dynamic and advanced models. Academic Press, London, UK, pp 76-82 Knape J, Arlt D, Barraquand F, Berg Å, Chevalier M, Pärt T, Ruete A, Zmihorski M (2018) Sensitivity of binomial N‐mixture models to overdispersion: The importance of assessing model fit. Methods Ecol Evol 9:2102–2114. https://doi.org/10.1111/2041-210X.13062 Kroodsma DE, Verner J (2020) Marsh Wren (Cistothorus palustris), version 1.0. In: Poole AF (ed) Birds of the world. Cornell Lab of Ornithology, Ithaca, NY, USA. https://doi.org/10.2173/bow.marwre.01 Leggett AH (2014) Distribution, abundance, and habitat associations of breeding marsh birds in Mississippi tidal marsh. Thesis, University of Georgia Lehmicke AJJ (2014) Breeding ecology of the Seaside Sparrow (Ammodramus maritimus) in Northern Gulf of Mexico tidal salt marshes. Dissertation, University of Georgia Lindenmayer DB, Likens GE (2009) Adaptive monitoring: a new paradigm for long-term research and monitoring. Trends Ecol Evol 24:482–486 Lindenmayer DB, Likens GE (2010) The science and application of ecological monitoring. Biol Conserv 143:1317–1328 May R, Van Dijk J, Wabakken P, Swenson JE, Linnell JD, Zimmermann B, Odden J, Pedersen HC, Andersen R, Landa A (2008) Habitat differentiation within the large‐carnivore community of Norway’s multiple‐use landscapes. J Appl Ecol 45:1382–1391. https://doi.org/10.1111/j.1365-2664.2008.01527.x Mouton EC, Felder DL (1996) Burrow Distributions and Population Estimates for the Fiddler Crabs Uca spinicarpa and Uca longisignalis in a Gulf of Mexico Salt Marsh. Estuaries 19:51. https://doi.org/10.2307/1352651 NASEM (National Academies of Science, Engineering, and Medicine) (2017) Effective monitoring to evaluate ecological restoration in the Gulf of Mexico. National Academies Press National Commission on the BP Deep Ocean Horizon Oil Spill and Offshore Drilling (2011) Deep water: The Gulf oil disaster and the future of offshore drilling. Report to the President. www.oilspillcommission.gov. Accessed May 2021 NOAA (National Oceanic and Atmospheric Administration) (2010) Coastal Change Analysis Program (C-CAP) regional land cover. NOAA Office for Coastal Management. Charleston, SC, USA. www.coast.noaa.gov/ccapftp. Accessed April 2021 NOAA (National Oceanic and Atmospheric Administration) (2017) Coastal Change Analysis Program (C-CAP) derived land cover - BETA. NOAA Office for Coastal Management. Charleston, SC, USA. www.coast.noaa.gov/ccapftp. Accessed April 2021 Norris K (2004) Managing threatened species: the ecological toolbox, evolutionary theory and declining‐population paradigm. J Appl Ecol 41:413–426. https://doi.org/10.1111/j.0021-8901.2004.00910.x Novak JN, Gaines KF, Cumbee Jr JC, Mills GL, Rodriguez-Navarro A, Romanek CS (2006) The Clapper Rail as an indicator species of estuarine marsh health. In: Greenberg R, Maldonado JE, Droege S, MacDonald MV (eds) Terrestrial vertebrates of tidal marshes: evolution, ecology, and conservation. Stud Avian Biol 32:270-281 Odum WE (1988) Comparative ecology of tidal freshwater and salt marshes. Annu. Rev. Ecol. Evol. Syst. 19:147–176 Pendleton EA, Barras JA, Williams SJ, Twichell DC (2010) Coastal vulnerability assessment of the Northern Gulf of Mexico to sea-level rise and coastal change. US Geological Survey Open-File Report 2010–1146. U.S. Department of the Interior, U.S. Geological Survey, Reston, VI, USA Pielou EC (1969) An introduction to mathematical ecology. Wiley-Interscience. New York, NY, USA Poole AF, Lowther PE, Gibbs JP, Reid FA, and Melvin SM (2020) Least bittern (Ixobrychus exilis), version 1.0. In: Poole AF (ed) Birds of the world. Cornell Lab of Ornithology, Ithaca, NY, USA. https://doi.org/10.2173/bow.leabit.01 Post W (1974) Functional Analysis of Space‐Related Behavior in the Seaside Sparrow. Ecology 55:564–575. https://doi.org/10.2307/1935147 R Core Team (2021) R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria Robel RJ, Briggs JN, Dayton AD, Hulbert LC (1970) Relationships between visual obstruction measurements and weight of grassland vegetation. J Range Manage 23:295–297 Royle JA (2004) Generalized estimators of avian abundance from count survey data. Anim Biodivers Conserv 27:375–386 Rush SA, Soehren EC, Woodrey MS, Graydon CL, Cooper RJ (2009) Occupancy of select marsh birds within northern Gulf of Mexico tidal marsh: current estimates and projected change. Wetlands 29:798–808 Rush SA, Mordecai R, Woodrey MS, Cooper RJ (2010a) Prey and habitat influences the movement of clapper rails in Northern Gulf coast estuaries. Waterbirds 33:389–396 Rush SA, Woodrey MS, Cooper RJ (2010b) Variation in the nesting habits of clapper rails in tidal marshes of the northern Gulf of Mexico. The Condor 112:356–362 Rush SA, Rodgers III J, Soehren EC, Trent JA (2019) Spatial and temporal changes in emergent marsh and associated marsh birds of the lower Mobile-Tensaw River Delta in Alabama, USA. Wetlands 39:1189–1201 Rush SA, Gaines KF, Eddleman WR, Conway CJ (2020) Clapper Rail (Rallus crepitans), version 1.0. In: Rodewald PG (ed) Birds of the world, Cornell Lab of Ornithology, Ithaca, NY, USA. https://doi.org/10.2173/bow.clarai11.01 SHARP (Saltmarsh Habitat and Avian Research Program) (2015). Vegetation Survey Protocol-2015. https://www.tidalmarshbirds.org. Accessed March 2021 SHARP (Saltmarsh Habitat and Avian Research Program) (2022). Saltmarsh habitat avian research program. https://www.tidalmarshbirds.org. Accessed March 2021 Schelske CL, Odum EP (1961) Mechanisms maintaining high productivity in Georgia estuaries. Proceedings of the Gulf and Caribbean Fisheries Institute 14:75-80 Snedden, G. A., Cable JE, Bjӧrn K (2013) Estuarine Geomorphology and Coastal Hydrology. In: Day JW Jr, Crump BC, Kemp WM, Yáñez-Arancibia A (eds) Estuarine ecology. Wiley-Blackwell, Hoboken, NJ, USA, pp 19-38 Spautz H, Nur N, Stralberg D, Chan Y (2006) Multiple-scale habitat relationships of tidal-marsh breeding birds in the San Francisco Bay estuary. Stud Avian Biol 32:247 Stout JP (1984) The ecology of irregularly flooded salt marshes of the northeastern Gulf of Mexico: a community profile. National Coastal Ecosystems Team, Division of Biological Services, Research and Development, U.S. Department of the Interior, Fish and Wildlife Service, Washington, DC, USA Torio DD, Chmura GL (2015) Impacts of sea level rise on marsh as fish habitat. Estuaries Coasts 38:1288–1303 USFWS (U.S. Fish and Wildlife Service), USGS (U.S. Geological Service) (2006) Strategic habitat conservation: final report of the National Ecological Assessment Team. http://www.fws.gov/science/doc/SHC_FinalRpt.pdf. Accessed March 2021 Veech JA, Ott JR, Troy JR (2016) Intrinsic heterogeneity in detection probability and its effect on N ‐mixture models. Methods Ecol Evol 7:1019–1028. https://doi.org/0.1111/2041-210X.12566 Weitzel SL, Feura JM, Iglay RB, Evans KO, Rush SA, Woodrey MS (2021) Distribution, abundance, and vegetation associations of birds in Mississippi tidal marshes during the non‐breeding season. J Field Ornith 92:231–245. https://doi.org/0.1111/jofo.12375 West RL, Hess GK (2020) Purple Gallinule (Porphyrio martinica), version 1.0. In: Poole A, Gill FB (eds) Birds of the world. Cornell Lab of Ornithology, Ithaca, NY, USA. https://doi. org/10.2173/bow. purgal2 White D (2007) Spatial grid networks. U.S. Environmental Protection Agency, Western Ecology Division, Corvallis, OR, USA. http://www.epa.gov/wed/pages/staff/white/getgrid.htm. Accessed 31 July 2018 Wieland RG (2007) Habitat types and associated ecological communities of the Grand Bay National Estuarine Research Reserve. In: Peterson MS, Waggy GL, Woodrey MS (eds) Grand Bay National Estuarine Research Reserve: an ecological characterization. Grand Bay National Estuarine Research Reserve, Moss Point, MS, USA, pp 104-147 Wiest WA, Correll MD, Olsen BJ, Elphick CS, Hodgman TP, Curson DR, Shriver WG (2016) Population estimates for tidal marsh birds of high conservation concern in the northeastern USA from a design-based survey. The Condor: Ornithol Appl 118:274–288 Wigand C, Roman CT (2012) North Atlantic Coastal Tidal Wetlands. In: Batzer DP, Baldwin AH (eds) Wetland Habitats of North America. University of California Press, Berkeley, CA, USA, pp 13–28 Woodrey MS, Rush SA, Cherry JA, Nuse BL, Cooper RJ, Lehmicke AJJ (2012) Understanding the potential impacts of global climate change on marsh birds in the Gulf of Mexico region. Wetlands 32:35–49 Woodrey MS, Fournier AM, Cooper RJ (2019) GoMAMN strategic bird monitoring guidelines: marsh birds. In: Wilson RR, Fournier AMV, Gleason JS, Lyons JE, Woodrey MS (eds) Strategic bird monitoring guidelines for the northern Gulf of Mexico. Mississippi Agricultural and Forestry Experiment Station Research Bulletin 1228, Mississippi State University, USA, pp 71–96 Yasukawa K, Searcy WA (2020) Red-winged blackbird (Agelaius phoeniceus), version 1.0. In: Rodewald RG (ed) Birds of the world. Cornell Lab of Ornithology, Ithaca, NY, USA. https://doi.org/10.2173/bow.rewbla.01 Yip DA, Bayne EM, Sólymos P, Campbell J, Proppe D (2017) Sound attenuation in forest and roadside environments: Implications for avian point-count surveys. The Condor: Ornithol Appl 119:73–84 Zuur AF, Ieno EN, Walker N, Saveliev AA, Smith GM (2009) Mixed effects models and extensions in ecology with R. Springer, New York, NY, USA. https://doi.org/10.1007/978-0-387-87458-6 Supplementary Files Andersonetal2025WetlandsSupplementalMaterial.docx Cite Share Download PDF Status: Published Journal Publication published 16 Oct, 2025 Read the published version in Wetlands → Version 1 posted Reviewers agreed at journal 20 Mar, 2025 Reviewers invited by journal 18 Mar, 2025 Editor invited by journal 13 Mar, 2025 Editor assigned by journal 12 Mar, 2025 First submitted to journal 12 Mar, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6214450","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":430603515,"identity":"8c6a3a84-6403-4d24-b478-ca158a1860b0","order_by":0,"name":"Rachel Anderson","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA3klEQVRIiWNgGAWjYHACNgaGAgYeBvbmA0COhAyRWgyAWniOJYC08BCtBajYB0QyENZi3n742YMPBodl+GfwfH51o8YC6MLDRzfg0yJzJs3ccIbBYR6J273brHOOAR3Gk5Z2A58WCYYcNmkegzQeA5mz24xz2IBaJHjM8Gvhf8Mm/QekRSLnmXHOP2K0SABtYTCwAWlhfpzbRpSWZ2aSPUAtEmeOmTHn9knwsBH0C3/yM4kfFRL2/O3Njz/nfKuT42c/fAyvFmTAJgEmiVUOAswfSFE9CkbBKBgFIwcAALu1PV9umHsQAAAAAElFTkSuQmCC","orcid":"https://orcid.org/0009-0000-1836-6607","institution":"Mississippi State University","correspondingAuthor":true,"prefix":"","firstName":"Rachel","middleName":"","lastName":"Anderson","suffix":""},{"id":430603516,"identity":"61a35879-ce5c-400c-874a-cdc9f430e7f7","order_by":1,"name":"Landon R. Jones","email":"","orcid":"","institution":"Mississippi State University","correspondingAuthor":false,"prefix":"","firstName":"Landon","middleName":"R.","lastName":"Jones","suffix":""},{"id":430603517,"identity":"f2fd2bee-09b4-4625-85a0-a9d1ca6488e3","order_by":2,"name":"Raymond B. Iglay","email":"","orcid":"","institution":"Mississippi State University","correspondingAuthor":false,"prefix":"","firstName":"Raymond","middleName":"B.","lastName":"Iglay","suffix":""},{"id":430603518,"identity":"78b81602-253b-45dd-9c04-0663a96b5e67","order_by":3,"name":"Kristine O. Evans","email":"","orcid":"","institution":"Mississippi State University","correspondingAuthor":false,"prefix":"","firstName":"Kristine","middleName":"O.","lastName":"Evans","suffix":""},{"id":430603519,"identity":"f16eb6ed-ed38-4bcc-9d68-e40f660c46f8","order_by":4,"name":"Jared M. Feura","email":"","orcid":"","institution":"Mississippi State University","correspondingAuthor":false,"prefix":"","firstName":"Jared","middleName":"M.","lastName":"Feura","suffix":""},{"id":430603520,"identity":"4a0de0b9-7e38-4d0c-ae63-ef60d6b5d255","order_by":5,"name":"Mark S. Woodrey","email":"","orcid":"","institution":"Mississippi State University","correspondingAuthor":false,"prefix":"","firstName":"Mark","middleName":"S.","lastName":"Woodrey","suffix":""}],"badges":[],"createdAt":"2025-03-12 19:05:28","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6214450/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6214450/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s13157-025-01987-9","type":"published","date":"2025-10-16T15:58:13+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":79342891,"identity":"f74c2cdc-f5aa-4e38-81c0-f7e15566ace9","added_by":"auto","created_at":"2025-03-27 08:58:28","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":280603,"visible":true,"origin":"","legend":"\u003cp\u003eTidal marsh complexes (\u003cem\u003en\u003c/em\u003e = 11) surveyed across coastal Mississippi for tidal marsh birds during breeding season, April-July 2021 and 2022. Each marsh complex is denoted with a unique color, and the black dots indicate point-count locations surveyed three times per year.\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6214450/v1/5a9272290b15c00bf3153e08.jpeg"},{"id":79342901,"identity":"e5b25ac9-864f-4d31-a46f-6e16287458eb","added_by":"auto","created_at":"2025-03-27 08:58:29","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":25388,"visible":true,"origin":"","legend":"\u003cp\u003eBroad scale landscape variables of a) landscape heterogeneity, b) distance to estuarine marsh (km), and c) distance to open water (km) were negatively associated with breeding marsh bird abundance (individuals per point count site [\u003cem\u003en\u003c/em\u003e = 3 visits] in 2021 and 2022) in coastal Mississippi. Species are denoted by unique colors with solid and dashed lines representing the estimated mean abundance for years 2021 and 2022, respectively. Shaded areas represent 95% confidence intervals with a darker shade for year 2022.\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-6214450/v1/fa32ec6d04a9e6e3c6739961.png"},{"id":79342887,"identity":"5f8cd54b-505a-4b3a-9130-483eb901e9c9","added_by":"auto","created_at":"2025-03-27 08:58:28","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":14296,"visible":true,"origin":"","legend":"\u003cp\u003eThe opposing relationships of Common Yellowthroat and Seaside Sparrow abundance (individuals per point count site [\u003cem\u003en\u003c/em\u003e= 3 visits] in 2021 and 2022) in coastal Mississippi with a) distance to palustrine wetlands (km) and b) distance to upland/developed land (km). Solid and dashed lines represent the estimated mean abundance for 2021 and 2022, respectively. Shaded areas represent 95% confidence intervals with a darker shade representing 2022.\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-6214450/v1/060198fb7d5e2493b37c3edb.png"},{"id":79342888,"identity":"42d8a7f7-e1be-4bc4-b91c-0853448b3929","added_by":"auto","created_at":"2025-03-27 08:58:28","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":18753,"visible":true,"origin":"","legend":"\u003cp\u003eVegetation height had a negative association with Clapper Rail, Gallinule, Seaside Sparrow, and Red-winged Blackbird abundance (individuals per point count site [\u003cem\u003en\u003c/em\u003e = 3 visits] in 2021 and 2022) in coastal Mississippi, but it was positively associated with Marsh Wren abundance. Species are denoted by unique colors with solid and dashed lines representing the estimated mean abundance for years 2021 and 2022, respectively. Shaded areas represent 95% confidence intervals with a darker shade representing 2022.\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-6214450/v1/07a95866bee9fb1f3e438cdb.png"},{"id":79342892,"identity":"3119445e-fc34-468e-9263-eeabf1bf5779","added_by":"auto","created_at":"2025-03-27 08:58:28","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":23820,"visible":true,"origin":"","legend":"\u003cp\u003eClapper Rail and Seaside Sparrow abundance (individuals per point count site [\u003cem\u003en\u003c/em\u003e = 3 visits] in 2021 and 2022) in coastal Mississippi was negatively associated with a) percent freshwater vegetation and positively associated with b) percent smooth cordgrass and c) percent black needlerush, but Red-winged Blackbird and Common Yellowthroat abundance was negatively impacted by the latter two variables. The gallinules were also negatively impacted by b) percent smooth cordgrass. Species are denoted by unique colors with solid and dashed lines representing the estimated mean abundance for years 2021 and 2022, respectively. The shaded areas represent 95% confidence intervals with a darker shade representing 2022.\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-6214450/v1/7a8932e9f5cb729375fb2b28.png"},{"id":93956192,"identity":"99932b83-d481-45ce-acee-7b70ab1e3561","added_by":"auto","created_at":"2025-10-20 16:11:25","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1328271,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6214450/v1/4d5cfad4-486c-49b7-bffa-0cb818950452.pdf"},{"id":79342917,"identity":"0c8ea261-8f41-4b49-8cf1-b6acba3bf908","added_by":"auto","created_at":"2025-03-27 08:58:31","extension":"docx","order_by":5,"title":"","display":"","copyAsset":false,"role":"supplement","size":4232345,"visible":true,"origin":"","legend":"","description":"","filename":"Andersonetal2025WetlandsSupplementalMaterial.docx","url":"https://assets-eu.researchsquare.com/files/rs-6214450/v1/1af6ce7ebafe87e2678ce5b2.docx"}],"financialInterests":"","formattedTitle":"Informing conservation planning with population estimates and divergent landscape associations of tidal marsh birds","fulltext":[{"header":"INTRODUCTION","content":"\u003cp\u003eTidal marsh ecosystems provide resources for many bird species, several of which are endemic to these geographically limited environments. Organisms inhabiting tidal marshes are regularly subjected to harsh environmental conditions linked to salinity, flooding, storm surge, and anthropogenic disturbances (Greenberg \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2006\u003c/span\u003e). Tidal marshes are highly productive due to rapid nutrient turnover rates, year-round production, and tidal energy flux (Schelske and Odum \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e1961\u003c/span\u003e; Ib\u0026aacute;\u0026ntilde;ez et al. \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). They also provide refuge from open water, supplying protection and resources for a range of resident and migratory wildlife species. Geomorphic, chemical, and physical processes such as macronutrient cycles, coastal erosion, elevation change, water circulation, and stratification of salinity and sediments all affect wetland structure and productivity, creating a variable and complex coastal environment (Bianchi \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Snedden et al. \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). The northern Gulf of Mexico region consists of a large complex of tidal marsh ecosystem that spans the interface of open water and land (Battaglia et al. \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). Although species diversity is relatively low, zonation patterns among vegetative communities create a heterogeneous landscape driven by local salinity and elevation gradients (Eleuterius \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e1972\u003c/span\u003e). These distinct vegetation zonation patterns occur across local and broad scales and influence the presence of birds and other wildlife by creating a diversity of foraging areas. Tidal marshes along the northern Gulf of Mexico are further impacted by anthropogenic stressors such oil spills, hurricanes, land subsidence, and high rates of relative sea-level rise (Pendleton et al. \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2010\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eTidal marsh birds are important bio indicators of the health of threatened coastal wetlands (Novak et al. \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2006\u003c/span\u003e). However, there is a general lack of knowledge of tidal marsh bird populations globally due to their low detection rates and lack of reliable estimates, especially for populations across the northern Gulf of Mexico (Woodrey et al. \u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). Establishing population estimates for conservation priority species is essential to understanding their current and future states and the factors influencing their persistence (USFWS and USGS 2006). Effective management of conservation lands is predicated on having robust population estimates and an understanding of species-specific landscape preferences across spatial scales (Norris \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2004\u003c/span\u003e; Johnston et al. \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Population estimates are not only critical to management decisions, but they can improve long-term monitoring efforts within an adaptive framework (Lindenmayer and Likens \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). Additionally, spatially-explicit predictions based on associations with landscape features are crucial for conservation planning for sensitive species in disturbance-prone ecosystems, such as tidal marsh birds.\u003c/p\u003e \u003cp\u003eThough difficult to determine, baseline population data for tidal marsh birds is greatly needed to be able to assess the impact of future perturbations and the effectiveness of restoration management in the northern Gulf of Mexico (NASEM 2017). The BP Deepwater Horizon Oil Spill in 2010 greatly impacted the Gulf of Mexico and coastal wetlands, making it one of the worst environmental disasters known to the United States (National Commission 2011). Haney et al. (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2014\u003c/span\u003e) estimated that ~\u0026thinsp;700,000 coastal birds died due to acute oil exposure across the northern Gulf of Mexico, but the population-level consequences for birds of conservation concern have yet to be determined. Following the BP Deepwater Horizon Oil Spill, the Gulf of Mexico Avian Monitoring Network (GoMAMN) was formed to maximize the usefulness of bird monitoring data for conservation and established long-term monitoring objectives to better understand coast-wide impacts, such as the Deepwater Horizon Oil Spill, on Gulf Coast bird populations, including secretive marsh birds (Woodrey et al. \u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). The GoMAMN recognized that standardized long-term monitoring is necessary to support bird conservation and restoration actions across the region. Thus, Woodrey et al. (\u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) recommended generating baseline population estimates for tidal marsh birds across the Gulf to assess restoration effectiveness over time and understand basic ecological processes within the tidal marsh community.\u003c/p\u003e \u003cp\u003eMost of the current knowledge about tidal marsh bird populations is from the Northeast and mid-Atlantic US coast (Saltmarsh Habitat and Avian Research Program [SHARP] 2022). However, Atlantic coast tidal marshes are generally smaller in area and differ in vegetative structure and tidal influence. Tidal marsh along the Atlantic coast is dominated by smooth cordgrass (\u003cem\u003eSpartina alterniflora\u003c/em\u003e; Wigand and Roman \u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e2012\u003c/span\u003e), which is not as abundant along the northern Gulf Coast, often only found in narrow zones near the water\u0026rsquo;s edge (Eleuterius \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e1972\u003c/span\u003e). Northern Gulf Coast marshes are irregularly flooded micro-tidal systems dominated by black needlerush (\u003cem\u003eJuncus roemerianus\u003c/em\u003e) and some areas receive frequent to intermittent freshwater input from major riverine systems (Stout \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e1984\u003c/span\u003e). These differences result in dissimilar vegetation and marsh bird assemblages between the two regions. It is generally understood that tidal marsh birds vary in abundance with salinity, vegetation, and marine/freshwater influence across their range. However, little is known about how local plant zonation and landscape variability affects marsh bird populations along the northern Gulf Coast. Therefore, the objectives of this study were to generate robust estimates of population size for the breeding tidal marsh bird community across the Mississippi Gulf Coast and determine how these birds respond to local and broad scale landscape factors. We hypothesized that tidal marsh birds in our study area would differ in their local and broad landscape associations. In addition, tidal marsh bird species found across the range from salt to fresh marsh (i.e., tidal marsh generalists) would have greater overall abundance. Thus, we predicted that Clapper Rails (\u003cem\u003eRallus crepitans\u003c/em\u003e) and Red-winged Blackbirds (\u003cem\u003eAgelaius phoeniceus\u003c/em\u003e) would have the greatest population size, but more restricted species such as Common Gallinules (\u003cem\u003eGallinula galeata\u003c/em\u003e), Purple Gallinules (\u003cem\u003ePorphyrio martinicus\u003c/em\u003e), Least Bitterns (\u003cem\u003eIxobrychus exilis\u003c/em\u003e), Marsh Wrens (\u003cem\u003eCistothorus palustris\u003c/em\u003e), Seaside Sparrows (\u003cem\u003eAmmospiza maritimus\u003c/em\u003e), and Common Yellowthroats (\u003cem\u003eGeothlypis trichas\u003c/em\u003e) would be less abundant.\u003c/p\u003e"},{"header":"METHODS","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy Area\u003c/h2\u003e \u003cp\u003eOur study area included 11 tidal marsh complexes across the Mississippi Gulf Coast (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e; Supplementary Material Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). These marsh complexes were a combination of marine-influenced and riverine-dominated marsh types and generally consisted of fresh (\u0026lt;\u0026thinsp;3 ppt salinity), intermediate (2\u0026ndash;8 ppt salinity), brackish (4\u0026ndash;10 ppt salinty), and salt (up to 29 ppt salinity) marsh zones (Eleuterius \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e1972\u003c/span\u003e). Elevation and inundation frequency contributed to tidal marsh plant zonation in these Mississippi marsh complexes. Riverine-dominated marshes generally supported a more diverse vegetative community due to the influx of freshwater and reduction of tidal influence (Odum \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e1988\u003c/span\u003e). The coastal region of Mississippi was composed of four major drainage systems of the Pearl, Pascagoula, Wolf and Jourdan River basins. However, some areas along the northern Gulf of Mexico received no major freshwater inflow and were mainly composed of brackish and salt marsh (e.g., Grand Bay National Estuarine Research Reserve [NERR]; Wieland \u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e2007\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eSampling Design\u003c/h3\u003e\n\u003cp\u003eWe used a spatially-balanced, probabilistic two-stage cluster sampling design to determine sampling locations (Johnson et al. \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). This approach has been successfully used for marsh bird monitoring along the Northeast US coast (Wiest et al. \u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) and was the basis for a winter-focused marsh bird population study across Mississippi tidal marshes (Weitzel et al. \u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Our sampling universe for coastal Mississippi consisted of estuarine emergent and estuarine scrub-shrub wetland land cover classes (hereafter estuarine marsh) from the 2010 30 m resolution Coastal Change Analysis Program (C-CAP; National Oceanic and Atmospheric Administration [NOAA] 2010). We used a Generalized Random Tessellation Stratification (GRTS) method to randomly select primary (PSUs) and secondary sampling units (SSUs) within a grid of 40 km\u003csup\u003e2\u003c/sup\u003e hexagons (i.e., North American continental hexagon grid; White \u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Supplementary Material Figure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). We established the PSU sampling frame by a priori identifying hexagons containing at least 10 ha of estuarine marsh resulting in a sampling frame of 52 PSU hexagons. Of these, we randomly selected 25 PSU hexagons plus an oversample of 10-hexagons using GRTS. We used the GRTS approach again to generate 30 (20 base\u0026thinsp;+\u0026thinsp;10 oversample) SSUs within each PSU hexagon, resulting in 870 potential survey points. Prior to our first sampling season, we visited each of the 870 potential surveys points to determine their suitability for inclusion in our sampling framework. Survey points were removed from our sampling frame if they were not located in estuarine marsh, were inaccessible by boat and/or on-foot, were within 400 meters from another survey point, or were located on private land. Based on these criteria, we eliminated 606 survey points, resulting in a spatially-balanced sampling frame of 264 survey points. After our initial review, we discovered another two survey points were inaccessible and removed them (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;262 total survey points).\u003c/p\u003e\n\u003ch3\u003eMarsh Bird Sampling\u003c/h3\u003e\n\u003cp\u003eWe conducted call-broadcast point counts at each survey point during the breeding season from April 14 \u0026ndash; July 21, 2021, and April 12 - July 21, 2022, visiting each survey point three times annually. Following the Standardized North American Breeding Marsh Bird Protocol (Conway \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2011\u003c/span\u003e), we collected data for focal and non-focal marsh bird species detected at each survey point during a 13 minute survey (i.e., 5 minute passive count and 8 minute call broadcast period). However, we only used data from the passive count for this study because of potential issues with inflated detection probabilities associated with birds being attracted to the sampling point during playback of conspecific calls. Focal species included secretive tidal marsh birds mainly from the family \u003cem\u003eRallidae\u003c/em\u003e, whereas non-focal species included passerine marsh obligate species (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). We estimated the distance (m) to each detected bird. Two observers conducted each survey with one observer recording focal species and the second observer recording non-focal species. Surveys began 30 minutes before sunrise and were completed by 4 hours after sunrise. We also recorded wind speed (km/hr) for each survey and did not survey during high winds (\u0026gt;\u0026thinsp;20 km/hr), sustained rain, or heavy fog.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMarsh bird species surveyed during point counts were categorized based on specialist/generalist status in the context of restriction to tidal marsh types. The number of individuals (\u003cem\u003en\u003c/em\u003e) for each species is the total individual detections over two years, three visits and 262-point count locations across the Mississippi coast. This list follows the phylogenetic order of the American Ornithological Society\u0026rsquo;s (AOS) Checklist of North and Middle American Birds (Chesser et al. \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAlpha code\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCommon name\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eScientific name\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eFocal/non-focal \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eClass\u0026nbsp;\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003eN\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePBGR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePied-billed Grebe\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003ePodilymbus podiceps\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eFocal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKIRA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eKing Rail\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eRallus elegans\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eFocal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCLRA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eClapper Rail\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eRallus crepitans\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eFocal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e7,595\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCOGA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCommon Gallinule\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eGallinula galeata\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eFocal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e288\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAMCO\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAmerican Coot\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eFulica americana\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eFocal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePUGA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePurple Gallinule\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003ePorphyrio martinicus\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eFocal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e74\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBLRA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBlack Rail\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eLaterallus jamaicensis\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eFocal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLEBI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLeast Bittern\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eIxobrychus exilis\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eFocal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e532\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMAWR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMarsh Wren\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eCistothorus palustris\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNon-focal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e382\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSESP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSeaside Sparrow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eAmmospiza maritimus\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNon-focal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1,159\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRWBL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRed-winged Blackbird\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eAgelaius phoeniceus\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNon-focal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e10,950\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCOGR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCommon Grackle\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eQuiscalus quiscula\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNon-focal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e58\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBTGR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBoat-tailed Grackle\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eQuiscalus major\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNon-focal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e672\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCOYE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCommon Yellowthroat\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eGeothlypis trichas\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNon-focal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1,493\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003e\u003csup\u003ea\u003c/sup\u003e Focal\u0026thinsp;=\u0026thinsp;secretive tidal marsh birds, for which call broadcasts were utilized to increase detectability; Non-focal\u0026thinsp;=\u0026thinsp;passerine obligates\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003e\u003csup\u003eb\u003c/sup\u003e Class 1\u0026thinsp;=\u0026thinsp;tidal marsh specialist restricted to specific type of tidal marsh (e.g., salt, brackish, or fresh); 2\u0026thinsp;=\u0026thinsp;tidal marsh specialist not restricted to a specific type of tidal marsh; 3\u0026thinsp;=\u0026thinsp;habitat generalist but restricted to specific type of tidal marsh; 4\u0026thinsp;=\u0026thinsp;habitat generalist not restricted to tidal marsh\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e\n\u003ch3\u003eBroad Scale Landscape Variable Sampling\u003c/h3\u003e\n\u003cp\u003eWe derived broad scale landscape variables using NOAA\u0026rsquo;s C-CAP land cover data (10 m resolution; NOAA 2017). We determined the land cover classes of interest based on known species-specific ecological preferences according to a tidal marsh bird literature review (Supplementary Material Table S2). We calculated the straight-line distances from each survey point to the nearest point of estuarine marsh, open water, upland/developed land, and palustrine wetlands using the Euclidean distance tool in ArcGIS Pro (ESRI \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Estimating the distance to land cover types assessed association with specific land cover types while accounting for edge effects (Conner et al \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2003\u003c/span\u003e). Because survey points were located within expanses of estuarine marsh (0 m values), we modified the Euclidean distance raster for this land cover type to include values within estuarine marsh as negative distance (m) values to the estuarine marsh edge (May et al. \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Jones et al. \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2022a\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003eb\u003c/span\u003e). This allowed us to better elucidate the relationship between marsh bird abundance and the extent of estuarine marsh.\u003c/p\u003e \u003cp\u003eWe accounted for variability in the composition of land cover types by calculating an index of heterogeneity for the land cover types of interest within a 200 m buffer around each point (Jones et al. \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2022a\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003eb\u003c/span\u003e). We used a 200 m buffer size because it assessed marsh composition within the local point count area and was used in previous Gulf region marsh bird studies (Rush et al. \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Leggett \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). This index measures land cover evenness based on the Shannon-Weaver index of species diversity and accounts for the relative proportions of land cover types (Pielou \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e1969\u003c/span\u003e). We extrapolated this measurement across the entire study area using a moving window analysis in program R (R Core Team \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Before modeling, we tested for any problematic correlations (coefficient\u0026thinsp;\u0026gt;\u0026thinsp;0.6) between landscape variables using a Pearson\u0026rsquo;s correlation matrix with the \u003cem\u003ercorr\u003c/em\u003e function in package \u003cem\u003eHmisc\u003c/em\u003e (Harrell \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) and by comparing variance inflation factors using the \u003cem\u003ecorvif\u003c/em\u003e function (Zuur et al. \u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e2009\u003c/span\u003e) in R (R Core Team \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e\n\u003ch3\u003eLocal Scale Vegetation Sampling\u003c/h3\u003e\n\u003cp\u003eWe conducted vegetation surveys at each survey point following a modified version of the SHARP (2015) point-intercept vegetation sampling design. We bisected a 100 m transect centered on each survey point to create a 50 m radius survey circle (Supplementary Material Figure S2). One observer standing at the survey point recorded the plant species observed and visually estimated the percent cover of each species that occupied\u0026thinsp;\u0026ge;\u0026thinsp;5% of the survey circle. If the entire area was not visible from the center of the 50 m radius circle, the observer walked along the transect to examine the entire plot. To characterize the vegetation structure of the plot, we measured the maximum height (cm) of each plant species that intercepted the 5-cm radius cylinder of a modified 2m tall Robel pole every 10 meters along the 100 m transect (Robel et al. \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e1970\u003c/span\u003e). We selected the variables vegetation height, percent black needlerush, percent smooth cordgrass, and percent freshwater vegetation (\u003cem\u003eSagittaria lancifolia, Sagittaria latifolia, Pontederia cordata, Cladium mariscus, Thelypteris palustris\u003c/em\u003e, and \u003cem\u003eAlternanthea philoxeroides\u003c/em\u003e) for analysis based on their purported biological relevance to marsh bird distributions (Supplementary Material Table S2). Vegetation height (cm) was the average height of all sampled vegetation species along our 100 m transect surveys at each survey point location.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eBird Abundance and Population Size\u003c/h2\u003e \u003cp\u003eWe estimated the abundance and population size of Clapper Rails, Common/Purple Gallinules, Least Bitterns, Marsh Wrens, Seaside Sparrows, Red-winged Blackbirds, and Common Yellowthroats across coastal Mississippi. We selected these species for analysis because they had sufficient detections (\u003cem\u003en\u003c/em\u003e\u0026thinsp;\u0026ge;\u0026thinsp;60) to generate reliable abundance estimates and represented a broad range of functional traits among the tidal marsh bird community, such as varying body size, habitat preferences, and foraging strategies. We combined detection data for Common and Purple Gallinule (hereafter gallinules) due to having low species-specific detections and similar habitat associations (Bannor and Kiviat \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; West and Hess \u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Supplementary Material Table S2). We used the \u003cem\u003epcount\u003c/em\u003e function (Royle \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2004\u003c/span\u003e) in the package \u003cem\u003eunmarked\u003c/em\u003e (Fiske and Chandler \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2011\u003c/span\u003e) in R (R Core Team \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) to generate hierarchical binomial N-mixture models that accounted for imperfect detection. The binomial N-mixture model required count data replicated across space and time and consisted of two sub-models that accounted for state and observation processes. We truncated detections by distance based on distance detection curves generated for each species (Supplementary Material Table S3).\u003c/p\u003e \u003cp\u003eWe fitted separate hierarchical N-mixture models using our locally-derived or remotely-sensed landscape variables to assess the local and broad scale effects of the landscape on tidal marsh bird abundance. We separated the local and broad scale variables because the broad scale abundance models were used to determine site-level abundance and further extrapolate abundance across the study area to estimate the population size of each species, whereas we used the local scale models to investigate variables that significantly impacted species-specific abundance at a finer scale. We fitted all models with observational covariates to account for variation in detection probability. We also included survey point as a random effect in the abundance sub-model to account for variation in abundance among replicated counts at each survey point. We analyzed abundance by year to avoid violating the model assumption of population closure (K\u0026eacute;ry and Royle \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) and to ensure confidence in our population estimates. Although combing years by \u0026ldquo;stacking\u0026rdquo; data in static N-mix models seems acceptable in some cases (K\u0026eacute;ry and Royle \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), it could lead to understated uncertainty, and we found that stacking did not improve our model fit results.\u003c/p\u003e \u003cp\u003eWe performed model selection by building model complexity in two stages (observation and abundance sub-models) and selecting the best model using Akaike Information Criterion (AIC). First, we fit the global observation sub-model with the effects of wind (Beaufort scale 0\u0026ndash;5), visit (occasion 1\u0026ndash;3), date (Julian day), and observer and identified models with ΔAIC\u0026thinsp;\u0026le;\u0026thinsp;2. We then chose the best model with the lowest AIC and fewest number of parameters (K). Next, we built upon the resulting model by adding the abundance covariates (local or landscape) and comparing AIC and K again for models with a ΔAIC\u0026thinsp;\u0026le;\u0026thinsp;2. If dropping a non-significant variable from the most parsimonious model improved AIC, it was removed. The final best model was identified by comparing AIC and fit between Poisson and Zero-inflated Poisson distributions. We assessed model fit by comparing model residuals using QQ plots with the \u003cem\u003enmixgof\u003c/em\u003e package (Knape et al. \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) in R. The QQ plots were generated for site-sum and observation randomized quantile (rq) residuals. Site-sum rq residuals were computed from aggregating counts across sites. Whereas, we derived the observation rq residuals from the observation sub-model. We evaluated model overdispersion, the presence of higher variance in the observed data than the expected variance of the model, by calculating an overdispersion parameter, the variance inflation factor (c-hat). Finally, using the broad scale models, we determined site-level abundance, or the estimated abundance within point count survey areas across our sites, within a 95% confidence interval using the \u003cem\u003eranef\u003c/em\u003e and \u003cem\u003epredict\u003c/em\u003e functions in package \u003cem\u003eunmarked\u003c/em\u003e (Fiske and Chandler \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2011\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eWe determined the total population size for each species by extrapolating the expected site-level abundance estimate across the study area using the function \u003cem\u003epredict\u003c/em\u003e in package \u003cem\u003eunmarked\u003c/em\u003e (Fiske and Chandler \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). Applying this prediction to every cell in our study area raster produced a spatially-explicit map of abundance values for each species (Supplementary Material Figures S4 \u0026ndash; S10). To determine the total abundance for each species across the survey area within a 95% confidence interval, we summed extrapolated abundance values from each raster cell in the \u003cem\u003epredict\u003c/em\u003e output in \u003cem\u003eunmarked\u003c/em\u003e (Fiske and Chandler \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). The total population size for each study species was calculated within estuarine marsh across coastal Mississippi as classified by NOAA\u0026rsquo;s C-CAP data. Additionally, we calculated Seaside Sparrow population size within salt marsh using the salt marsh land cover data layer from the U.S. Geological Survey Delineation of Marsh Types (Enwright et al. \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2014\u003c/span\u003e), because Seaside Sparrows are known to be restricted to areas of saline marsh (Greenlaw and Post 2022).\u003c/p\u003e \u003c/div\u003e"},{"header":"RESULTS","content":"\u003cp\u003eWe detected 22,292 marsh birds over 1,572 surveys across the survey period of April-July, 2021 and 2022 in coastal Mississippi (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The mean number of individual marsh birds per survey point differed among marsh complexes, with the greatest mean detections (across visits and years) at Grand Bay NERR (106\u0026thinsp;\u0026plusmn;\u0026thinsp;18, \u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;2,252). Although the Pascagoula (97\u0026thinsp;\u0026plusmn;\u0026thinsp;38, \u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;8,013) and Hancock County marshes (84\u0026thinsp;\u0026plusmn;\u0026thinsp;22, \u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;3,351) were the largest marsh areas surveyed (Supplementary Material Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e), smaller sites such as Deer Island (83\u0026thinsp;\u0026plusmn;\u0026thinsp;39, \u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;248), Graveline (79\u0026thinsp;\u0026plusmn;\u0026thinsp;30, \u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1,424), and Gulf Park Estates (73\u0026thinsp;\u0026plusmn;\u0026thinsp;17, \u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;439) had comparable mean marsh bird detections per survey point across years.\u003c/p\u003e\n\u003ch3\u003eTidal Marsh Bird Abundance and Population Estimates\u003c/h3\u003e\n\u003cp\u003eRed-winged Blackbirds and Clapper Rails were the most abundant species among our survey sites (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Population size estimates increased for Clapper Rails (17%), gallinules (471%), Least Bitterns (130%), and Red-winged Blackbirds (112%) from 2021 to 2022, but Common Yellowthroat population size decreased by 30%. Interestingly, the total population size increased for Marsh Wrens (56%) and Seaside Sparrows (256%) from 2021 to 2022, but their site-level abundance decreased between survey years (83% and 60%, respectively). Site-level abundance also declined between years for the Common Yellowthroat (91%). QQ plots of randomized-quantile residuals against standardized normal residuals indicated adequate fit for each model and species between years, but there was some deviation from the identity line at large quantile values (Supplementary Material Figure S3). Overdispersion values (c-hat) slightly deviated from one across all models (range\u0026thinsp;=\u0026thinsp;0.69\u0026ndash;1.74), but there was no systematic indication of problematic overdispersion (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSite-level abundance and population size model results vary in 2021 and 2022 for Clapper Rails, Common and Purple Gallinules, Least Bitterns, Marsh Wrens, Seaside Sparrows, Red-winged Blackbirds, and Common Yellowthroats. Abundance and population size per year is reported with a 95% confidence interval. The best ranked models for each species are separated into observation and abundance sub-models. The top model per year was chosen from a candidate set of models within 2 ΔAIC units based on the number of parameters (K) and AIC. Each models\u0026rsquo; associated c-hat values indicated low amounts of overdispersion.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"9\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" 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align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2,487 [2,394-2,578]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e14,124 [4,284\u0026thinsp;\u0026minus;\u0026thinsp;46,623]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003edate\u0026thinsp;+\u0026thinsp;wind\u0026thinsp;+\u0026thinsp;visit\u0026thinsp;+\u0026thinsp;observer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLH\u0026thinsp;+\u0026thinsp;Watdist\u0026thinsp;+\u0026thinsp;Estdist+(1|Point_ID)\u003c/p\u003e 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\u003cp\u003eEstdist\u0026thinsp;+\u0026thinsp;Watdist+(1|Point_ID)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2,555 [2,460-2,650]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e6,571 [2,424\u0026thinsp;\u0026minus;\u0026thinsp;18,790]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003edate\u0026thinsp;+\u0026thinsp;visit\u0026thinsp;+\u0026thinsp;observer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLH\u0026thinsp;+\u0026thinsp;Watdist\u0026thinsp;+\u0026thinsp;Estdist+(1|Point_ID)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e436 [404\u0026ndash;471]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e10,282 [3,659\u0026thinsp;\u0026minus;\u0026thinsp;29,545]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSESP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003edate\u0026thinsp;+\u0026thinsp;wind\u0026thinsp;+\u0026thinsp;observer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLH\u0026thinsp;+\u0026thinsp;Estdist\u0026thinsp;+\u0026thinsp;Updist\u0026thinsp;+\u0026thinsp;Paldist+(1|Point_ID)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1,528 [1,467-1,590]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e14,386 [3,804\u0026thinsp;\u0026minus;\u0026thinsp;502,293]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003edate\u0026thinsp;+\u0026thinsp;wind\u0026thinsp;+\u0026thinsp;visit\u0026thinsp;+\u0026thinsp;observer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eEstdist\u0026thinsp;+\u0026thinsp;Paldist+(1|Point_ID)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eZIP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e615 [563\u0026ndash;673]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e51,249 [9,558\u0026thinsp;\u0026minus;\u0026thinsp;479,050]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRWBL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003edate\u0026thinsp;+\u0026thinsp;wind\u0026thinsp;+\u0026thinsp;visit\u0026thinsp;+\u0026thinsp;observer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLH\u0026thinsp;+\u0026thinsp;Watdist\u0026thinsp;+\u0026thinsp;Estdist+(1|Point_ID)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e5,568 [5,457-5,679]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e79,517 [55,998\u0026thinsp;\u0026minus;\u0026thinsp;112,969]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003edate\u0026thinsp;+\u0026thinsp;wind\u0026thinsp;+\u0026thinsp;visit\u0026thinsp;+\u0026thinsp;observer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLH\u0026thinsp;+\u0026thinsp;Watdist+(1|Point_ID)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e11,083 [10,757\u0026thinsp;\u0026minus;\u0026thinsp;11,421]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e168,196 [128,397\u0026thinsp;\u0026minus;\u0026thinsp;220,587]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCOYE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003edate\u0026thinsp;+\u0026thinsp;wind\u0026thinsp;+\u0026thinsp;visit\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLH\u0026thinsp;+\u0026thinsp;Paldist\u0026thinsp;+\u0026thinsp;Updist+(1|Point_ID)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eZIP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e979 [929-1,032]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e8,613 [4,089\u0026thinsp;\u0026minus;\u0026thinsp;18,229]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003edate\u0026thinsp;+\u0026thinsp;wind\u0026thinsp;+\u0026thinsp;visit\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLH\u0026thinsp;+\u0026thinsp;Paldist+(1|Point_ID)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eZIP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e592 [547\u0026ndash;633]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e6,055 [3,783-9,741]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eLH\u0026thinsp;=\u0026thinsp;landscape heterogeneity, Watdist\u0026thinsp;=\u0026thinsp;distance to open water, Estdist\u0026thinsp;=\u0026thinsp;distance to estuarine marsh, Updist\u0026thinsp;=\u0026thinsp;distance to developed/uplands, Paldist\u0026thinsp;=\u0026thinsp;distance to palustrine wetlands, (1|Point_ID)\u0026thinsp;=\u0026thinsp;random effect of point count survey ID, ZIP\u0026thinsp;=\u0026thinsp;zero-inflated Poisson, P\u0026thinsp;=\u0026thinsp;Poisson, c-hat\u0026thinsp;=\u0026thinsp;overdispersion or variance inflation factor.\u003c/p\u003e \u003cp\u003e*CPGA\u0026thinsp;=\u0026thinsp;combination of the COGA and PUGA alpha codes\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eLandscape Associations\u003c/h2\u003e \u003cp\u003e \u003cb\u003eBroad scale.\u003c/b\u003e Landscape heterogeneity, distance to estuarine marsh, and distance to open water were most often negatively associated with tidal marsh bird abundance (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Every study species had a negative relationship with increasing landscape heterogeneity (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ea). As distance to estuarine marsh increased, marsh bird abundance decreased, except for Common Yellowthroats and gallinules, which were unaffected by distance to estuarine marsh (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eb). Similar to distance to estuarine marsh, marsh bird abundance decreased with distance further from open water, except for gallinules, Seaside Sparrows, and Common Yellowthroats (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ec). Seaside Sparrow abundance increased with greater distance from palustrine wetlands (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ea) and upland/developed lands (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eb), whereas Common Yellowthroat abundance decreased for both land cover types (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Least Bittern and Marsh Wren landscape associations differed between years. The only landscape variable affecting Least Bittern abundance in the first year of the study was landscape heterogeneity (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e; Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ea). Marsh Wrens were negatively impacted by distance to estuarine marsh in the first year but not in the second (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eb). In the second year, Marsh Wren abundance became negatively associated with landscape heterogeneity (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ea).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eLocal scale.\u003c/b\u003e Vegetation height, percent black needlerush, and percent smooth cordgrass were often associated with marsh bird abundance (Figs.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e and \u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). Increasing vegetation height negatively impacted Clapper Rail, gallinule, Seaside Sparrow, and Red-winged Blackbird abundance, but it positively impacted Marsh Wren abundance (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Clapper Rail, Seaside Sparrow, and Red-winged Blackbird abundance decreased with increasing percentage of freshwater vegetation (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ea). Clapper Rail and Seaside Sparrow abundance was positively associated with smooth cordgrass (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eb) and black needlerush (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ec), whereas gallinules, Red-winged Blackbirds, and Common Yellowthroats were negatively associated with both plant species (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eb and \u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ec). Marsh Wrens and Least Bitterns were the only species not impacted by black needlerush or smooth cordgrass percent coverages. Our local scale variables had no relationships with Least Bittern abundance.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003eWe generated baseline population estimates for eight breeding tidal marsh bird species that allow for evaluating population change over time in coastal Mississippi with a sampling design that is applicable to other marsh systems along the northern Gulf Coast. Our work demonstrates that broad scale population estimates can be derived using standardized approaches in Gulf of Mexico marshes which are a fundamentally different marsh ecosystem from Northeast US coast tidal marsh systems (Wiest et al. \u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Further, our models captured the diverse marsh systems of Mississippi and provide complementary information regarding plant species composition and zonation at both local and broad spatial scales. We expected and confirmed that Red-winged Blackbirds and Clapper Rails were the most abundant breeding tidal marsh birds across coastal Mississippi, mainly because they are more generalist species, occurring across the range of marsh types (Rush et al. \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Yasukawa and Searcy \u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Whereas no other abundance estimates for breeding marsh birds are available for the northern Gulf of Mexico, Seaside Sparrows (230,000; 95% CI\u0026thinsp;=\u0026thinsp;174,000-286,000) were estimated to be the most abundant tidal marsh bird specialist along the northeast Atlantic coast, and Clapper Rails (151,000; 95% CI\u0026thinsp;=\u0026thinsp;90,000-212,000) were the second most abundant (Wiest et al. \u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). The difference in abundance between the two regions is likely due to the greater tidal amplitude in the Northeast which results in a greater proportion of salt marsh in their smaller tidal marsh complexes across the region.\u003c/p\u003e \u003cp\u003ePrior to our study, breeding marsh bird population estimates were nonexistent for coastal Mississippi. However, Weitzel et al. (\u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) generated winter population estimates for non-breeding tidal marsh birds across the Mississippi coast using a distance-sampling approach. Of the eight species in our study, Clapper Rails, Marsh Wrens, Seaside Sparrows, Red-winged Blackbirds, and Common Yellowthroats were the only species present across seasons. Clapper Rail and Common Yellowthroat populations were higher during the breeding season, likely a reflection of a decrease in detectability of rails during the non-breeding season when they are less prone to call. For Common Yellowthroats, this lower abundance probably reflects the migratory nature of individuals found along the coast during the breeding season. Marsh Wrens, Seaside Sparrows, and Red-winged Blackbirds were more abundant during the non-breeding season than during the breeding season. This result is consistent with what we know about the life history of these three species being short-distance migrants wintering primarily along the northern coast of the Gulf of Mexico (Hamel \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e1992\u003c/span\u003e). Comparisons of abundance are informative, because we understand breeding and nonbreeding populations to be distinct and expect population estimates and landscape relationships to differ among species, seasons, and geographies (Woodrey et al. \u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Therefore, comprehensive management plans will benefit from intra-annual population data for each species.\u003c/p\u003e \u003cp\u003eThe confidence around our population estimates varied across species likely due to differences in data types and detection probabilities. Seaside Sparrows had extremely broad confidence intervals, leading us to have less confidence in our population estimates for this species. These broad confidence intervals could result from their high detection probability (\u0026gt;\u0026thinsp;0.50) and semi-colonial behavior during the breeding season (Post \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e1974\u003c/span\u003e). A clumped distribution could violate independence assumptions of N-mixture models (Joseph et al. 2009). However, our site-level abundance estimates were less concerning than our broad, coast-wide extrapolations. Seaside Sparrows, as salt marsh specialists, are sensitive to local scale salinity gradients across the landscape, as confirmed by our local scale models. We observed Seaside Sparrows to congregate in higher salinity marsh areas that are not distinguishable in our coast-wide remotely-sensed landscape maps. Unfortunately, C-CAP classifies marsh broadly into areas of estuarine emergent or palustrine wetlands but does not include brackish or salt marsh cover types. In an attempt to account for the lack of fine scale marsh types, we used the salt marsh land cover data layer from the U.S. Geological Survey Delineation of Marsh Types Report (Enwright et al. \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Although this map had a salt marsh land cover class, it was not fine enough to distinguish zones of smooth cordgrass marsh where we know Seaside Sparrows occur locally. Future efforts should account for non-independent data as best possible and include fine scale land cover classifications when determining regional Seaside Sparrow populations.\u003c/p\u003e \u003cp\u003eSeveral of our study species showed high inter-annual variability in their abundance estimates. Marsh Wren populations are known to vary considerably by year and season across their North American range (Kroodsma and Verner \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), and our results reflect this known annual variation in abundance. In addition, Least Bittern, gallinule, Seaside Sparrow, and Red-winged Blackbird estimates also varied considerably between years. Rush et al. (\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) found that average densities declined from 2004 to 2015 for Least Bitterns and Common Gallinules in the Mobile-Tensaw River Delta in Alabama and suggest this change over time could be linked to landscape change unaccounted for. Although our broad scale landscape data did not change between survey years, relationships between species abundance and landscape features did change in some cases, which could explain the variation in abundance between years as well as between site-level abundance and total population size. Landscape composition/structure, hydrology, salinity, tidal influence, and resource availability vary across tidal marsh systems annually, and these factors likely contributed to the variability in marsh bird populations as revealed in our study and elsewhere. The observed inter-annual variation in our results highlights the importance of further examining dynamic spatiotemporal factors and conducting long-term monitoring of bird populations (Lindenmayer and Likens \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). Overall, we determined robust population estimates by minimizing error and avoiding model assumption violations through the use of our systematic sampling design, standardized sampling methodology, and a modelling approach that considers imperfect detection. However, we acknowledge that N-mixture models can be sensitive to low detection probabilities and unmodeled heterogeneity (Veech et al. \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; K\u0026eacute;ry and Royle \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Duarte et al. \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). We determined model fit was adequate across our study species and there were no indications of problematic overdispersion in our models. Nevertheless, our results should be interpreted with caution, and future efforts for implementing hierarchical models of abundance should consider model sensitivity.\u003c/p\u003e \u003cp\u003eOur results indicated that breeding tidal marsh birds in coastal Mississippi prefer more homogenous land cover composition and closer distances to estuarine marsh with open water nearby. This observed negative relationship with landscape heterogeneity seems to contradict Weitzel et al. (\u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) who found positive associations of non-breeding Clapper Rail and Marsh Wren abundance with increasing vegetation ecotones. Whereas that study was focused on the non-breeding season in coastal Mississippi and examined the local diversity of ecotone types, our study in the breeding season used a heterogeneity index that essentially measured the evenness of broad land cover types according to remotely sensed land cover data (e.g., estuarine wetlands, palustrine wetlands, developed land/uplands, and open water). Additionally, this could be a result of differences among breeding and non-breeding habitat, as the marsh landscape is more robust and varied in the breeding season (Woodrey et al. \u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Previous studies have shown positive relationships between marsh bird abundance and the diversity of vegetation types within inland, palustrine marsh (Alexander and Hepp \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2014\u003c/span\u003e), but there is a lack of research on how land cover heterogeneity affects estuarine marsh bird abundance in terms of the proportion of land cover types. On a broad scale, our study species likely prefer a single marsh community type as opposed to many, because they are estuarine marsh obligates and have been positively associated with larger, contiguous marsh patches in other parts of their range (Benoit and Askins \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2002\u003c/span\u003e; Spautz et al. \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e2006\u003c/span\u003e). Given our remotely sensed data only captured broad land cover types, we recommend examining this relationship with finer scale landscape data that better reflects the diversity of land cover types within tidal marsh.\u003c/p\u003e \u003cp\u003eMost of our study species were negatively associated with greater distances to estuarine marsh and open water, except for Common Yellowthroats and gallinules. Although present in estuarine marsh, these species prefer lower salinity, palustrine wetlands across their range likely resulting in their abundances being more driven by vegetation structure and density (Bannor and Kiviat \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Guzy and Ritchison \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; West and Hess \u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). For example, Common Yellowthroats use diverse landscape types across their range, from wetlands to dry uplands; however, they generally seem to prefer dense undergrowth and thickets (Guzy and Ritchison \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). While Common Yellowthroat abundance increased with proximity to palustrine wetlands and upland/developed lands, Seaside Sparrows expressed their status as salt-marsh specialists with a sensitivity to coastal development/uplands and palustrine wetlands, which is consistent with previous findings in Mississippi (Rush et al. \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Leggett \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Because Clapper Rails, Least Bitterns, and Marsh Wrens use the water\u0026rsquo;s edge for foraging (Kroodsma and Verner \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Poole et al. \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Rush et al. \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), we expected to see a decrease in their abundance as distance to open water increased. The interspersion of open water via tidal creeks likely provides adequate foraging habitat for these species. More research regarding preference for extent and interspersion of open water is warranted for these species to better direct management actions.\u003c/p\u003e \u003cp\u003eOur multi-scale landscape data revealed some unexpected but complementary relationships across marsh bird species. For example, our models indicated the broad scale effect of proximity to palustrine wetlands did not influence breeding Clapper Rail abundance, but the local presence of freshwater plants negatively impacted Clapper Rail abundance. Fiddler crab densities vary across marsh vegetation and salinity (Mouton and Felder \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e1996\u003c/span\u003e). Therefore, the presence of freshwater plants could mean fewer fiddler crabs, the preferred prey of breeding Clapper Rails in Mississippi (Rush et al. \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2010a\u003c/span\u003e). Similarly, broad scale predictors of distance to estuarine or palustrine wetlands had no significant effect on gallinule abundance, but the increasing percentage of smooth cordgrass negatively impacted their populations. In North America, gallinules breed in brackish and palustrine wetlands with emergent and semi-aquatic vegetation (Bannor and Kiviat \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; West and Hess \u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) avoiding high salinity wetlands (\u0026gt;\u0026thinsp;10 ppt). Ultimately, these results reveal the importance of examining landscape variables at both broad and local scales.\u003c/p\u003e \u003cp\u003eVegetation height only had a positive association with Marsh Wrens, whereas all remaining study species exhibited negative associations. Marsh Wrens prefer nest sites in taller vegetation throughout other parts of their range (Kroodsma and Verner \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). The negative effect of increasing vegetation height among our other study species could indicate that the threat of nest flooding may not be as significant across coastal Mississippi, as marsh birds often nest in taller vegetation to avoid flooding (Greenberg et al. \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Rush et al. \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2010b\u003c/span\u003e). However, previous studies showed that Seaside Sparrows chose nest sites with taller vegetation compared to random points at the Grand Bay NERR and Pascagoula River marsh complexes in Mississippi (Lehmicke \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). The negative relationship observed in our study could be an artifact of preference for higher salinity marsh vegetation (e.g., smooth cordgrass) overall, which is sparser and shorter than lower salinity marsh vegetation (Eleuterius \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e1972\u003c/span\u003e). Alternatively, marsh birds like Seaside Sparrows and Clapper Rails may not be as impacted by predation pressure due to the concealment of their nests with woven canopies and can afford to nest in shorter vegetation. Last, greater vegetation height and denser structure could decrease detectability due to sound attenuation (Yip et al. \u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e2017\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eRush et al. (\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2009\u003c/span\u003e) predicted that an increase in halophytic or salinity tolerant plants may reduce Least Bittern, Marsh Wren, and Common Yellowthroat occupancy while increasing the presence of Clapper Rails and Seaside Sparrows. Our results indicated that Clapper Rail and Seaside Sparrow abundance increased with increasing extent of estuarine marsh and salinity tolerant plants like black needlerush and smooth cordgrass; however, their abundance also declined with upland/developed land encroaching on expansive tidal marshes. Upland and developed land cover may indirectly affect marsh bird abundance due to increased predation and anthropogenic influence, while it may also directly affect it through fragmentation of marsh and by presenting a barrier to movement. Common Yellowthroat, gallinule, and Red-winged Blackbird abundance was negatively impacted by the saline tolerant plant, smooth cordgrass, in our study. Furthermore, Common Yellowthroat abundance increased in proximity to palustrine wetlands. Therefore, encroaching salinity due to sea level rise may negatively impact the future distribution and abundance of Common Yellowthroats, gallinules, and Red-winged blackbirds.\u003c/p\u003e \u003cp\u003eInvestigating relationships between tidal marsh birds and the landscape of the northern Gulf Coast is of particular importance in the context of increasing pressures on coastal areas. Concomitant anthropogenic, climatic, and other natural disturbances will continue to affect marsh bird population resilience as marshes are converted to open water and saline water encroaches landward (Erwin et al. \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2006\u003c/span\u003e). As marshes slowly migrate landward, terrestrial barriers among topography and developed land will further inhibit marsh movement and connectivity (Enwright et al. \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). This process is broadly referred to as \u0026ldquo;coastal squeeze\u0026rdquo; (Doody \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2004\u003c/span\u003e), and it threatens to reduce marsh primary productivity, availability of nesting habitat for breeding birds, and food availability for breeding marsh birds (Hughes \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2004\u003c/span\u003e; Torio and Chmura \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). There is a great need to better understand how the cumulative effects of landscape change will affect marsh bird populations into the future, and special attention is required for our study species, especially gallinules, Least Bitterns, and Marsh Wrens, for which temporal population dynamics and landscape associations remain poorly understood.\u003c/p\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eManagement Implications\u003c/h2\u003e \u003cp\u003eWhile the primary objective of this research was to generate robust population estimates for breeding tidal marsh birds, these estimates also have implications for refining state and regional monitoring programs such as those priorities identified by the Gulf of Mexico Avian Monitoring Network (Woodrey et al. \u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Here we highlight a few of the more important results from this work that will inform a Gulf of Mexico-wide marsh bird monitoring program. For this study, we calculated samples sizes using sampling point estimation and simulation modelling (Iglay et al. \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). We incorporated both data gleaned from published literature or generated distance analysis statistics using raw data from a limited number of tidal marshes along the Mississippi and Alabama coasts. Given the lack of published data and the patchy geographical representation of our raw data, our confidence in our ability to precisely determine a necessary sample size for the current study was limited. However, with the large number of point count surveys and their broad geographic distribution across the Mississippi coast, we now have the data necessary to develop a more robust sample size estimate. Further, Gulf-wide land cover datasets used in other regions such as the northeastern U.S. are not available for the entire Gulf region. Thus, we had to apply a more non-traditional Gulf-wide land cover classification scheme, namely C-CAP data, for determining our sampling frame. Fortunately, our results indicate that the two-stage sampling design based on C-CAP land cover data can be used to successfully design a sampling framework to generate robust population estimates for breeding marsh birds across the Mississippi coast, and likely expand across the entire Gulf of Mexico region.\u003c/p\u003e \u003cp\u003eOur population estimates provide baseline conditions prior to proposed conservation actions or unexpected incidents, such as oil spills. Since we did not have baseline data prior to the Deepwater Horizon Oil Spill, we were not able to confidently assess the impact on tidal marsh birds across the region. However, this study now provides the estimates necessary for quantifying any future breeding tidal marsh bird population-level impacts. Additionally, these estimates improve marsh bird mortality assessments that are inherently difficult to quantify since most tidal marsh birds reside in the marsh interior and are thus underestimated in shoreline mortality surveys (Deepwater Horizon Natural Resource Damage Assessment Trustees \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Furthermore, these population estimates allow managers to measure the impact of their management activities and identify the importance of maintaining hydrologic conditions and resulting salinity gradients within tidal marshes.\u003c/p\u003e \u003cp\u003eTidal marsh restoration plans should focus on building larger estuarine marsh expanses and protecting the existing expansive, intact marsh complexes. Estuarine marsh with high amounts of open water interspersion meet the needs of both tidal marsh generalists and specialists across the Mississippi coast. When considering local vegetation structure, managers should incorporate shorter vegetation height by planting and protecting expansive short-form smooth cordgrass areas. However, smooth cordgrass monocultures should be avoided at a broad scale as this may not be beneficial for all tidal marsh species, especially Marsh Wrens, Common Yellowthroats, and gallinules. Our results also show that consideration of adjacent land cover types is needed when prioritizing or creating marsh areas. If a salt marsh specialist like the Seaside Sparrow is the target species for restoration/management actions, managers should avoid areas in close proximity to palustrine wetlands, upland/developed lands, and areas with freshwater vegetation locally. If the entire marsh bird community is the target for restoration/management actions, managers will need to consider diverse marsh types and prioritize large areas that include patches of smooth cordgrass marsh greater distances from palustrine and upland/developed land cover. Our study further revealed that we should consider fine-scale landscape and salinity data for salt-marsh specialists like Seaside Sparrows and other marsh structural variables like vegetation density and water levels. Clearly, the often divergent habitat preferences of tidal marsh obligate species should be considered in the context of how to effectively promote marsh bird populations and thus, tidal marsh health. Estuarine tidal marsh restoration projects across the Gulf Coast should focus on monitoring the response of salt and brackish marsh specialists such as the Clapper Rail and Seaside Sparrow. However, freshwater associated tidal marsh species, like gallinules, Least Bitterns, Marsh Wrens, and Common Yellowthroats should be considered for projects focused on providing a broader range of marsh salinity types. Finally, we recommend marsh bird monitoring be expanded to areas of palustrine wetlands across the Mississippi Gulf Coast to better understand the impact of freshwater influence on gallinules, Least Bitterns, Marsh Wrens, and Common Yellowthroats. Robust monitoring of breeding tidal marsh birds can be successfully expanded across the entire Gulf of Mexico Region, and we recommend this idea be discussed, explored, and considered by the conservation community across the region.\u003c/p\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003ch2\u003eCompeting Interests\u003c/h2\u003e \u003cp\u003eThe authors have no relevant financial or non-financial interests to disclose.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eThis work was funded with support from the Mississippi Department of Environmental Quality, via the National Fish and Wildlife Foundation Gulf Environmental Benefit Fund [Grant #335414] to Mississippi State University (RBI, KOE, MSW). Additional funding support included National Oceanic and Atmospheric Administration [Awards #NA17NOS4200045, #NA18NOS4200063] to the Mississippi Department of Marine Resources\u0026rsquo; Grand Bay National Estuarine Research Reserve (MSW), and the Mississippi Department of Marine Resources [Contract #8200025414] to Mississippi State University (MSW). This publication is a contribution of the Mississippi Agricultural and Forestry Experiment Station, Forest and Wildlife Research Center at Mississippi State University, and based upon work supported by the National Institute of Food and Agriculture, U.S. Department of Agriculture, Hatch project under accession number 7002261 (MSW).\u003c/p\u003e\u003ch2\u003eAuthor Contributions\u003c/h2\u003e \u003cp\u003eMSW, RBI, and KOE developed the study design and supervised the research. JMF assisted with sampling design and methods. LRJ assisted with analytical methods. RVA collected data, conducted the analysis, and wrote the paper.\u003c/p\u003e\u003ch2\u003eACKNOWLEDGMENTS\u003c/h2\u003e \u003cp\u003eWe want to thank all of those who assisted in this project and conducted the necessary and often strenuous field work. We also thank personnel from the Saltmarsh Habitat \u0026amp; Avian Research Program (SHARP) who provided advice for implementing a large-scale marsh bird survey. This work was funded with support from the Mississippi Department of Environmental Quality, via the National Fish and Wildlife Foundation Gulf Environmental Benefit Fund [Grant #335414] to Mississippi State University (RBI, KOE, MSW). Additional funding support included National Oceanic and Atmospheric Administration [Awards #NA17NOS4200045, #NA18NOS4200063] to the Mississippi Department of Marine Resources\u0026rsquo; Grand Bay National Estuarine Research Reserve (MSW), and the Mississippi Department of Marine Resources [Contract #8200025414] to Mississippi State University (MSW). This publication is a contribution of the Mississippi Agricultural and Forestry Experiment Station, Forest and Wildlife Research Center at Mississippi State University, and based upon work supported by the National Institute of Food and Agriculture, U.S. Department of Agriculture, Hatch project under accession number 7002261 (MSW). Any opinions, findings, conclusions, or recommendations expressed in this publication are those of the author(s) and do not necessarily reflect the view of the U.S. Department of Agriculture.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e \u003cp\u003eData generated and analyzed during this study are available in Mississippi State University\u0026rsquo;s Institutional Repository, Scholars Junction, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://scholarsjunction.msstate.edu/td/6312/\u003c/span\u003e\u003cspan address=\"https://scholarsjunction.msstate.edu/td/6312/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAlexander BW, Hepp GR (2014) Estimating effects of habitat characteristics on abundances of three species of secretive marsh birds in Central Florida. Waterbirds 37:274\u0026ndash;285\u003c/li\u003e\n\u003cli\u003eBannor BK, Kiviat E (2020) Common Gallinule (Gallinula galeata), version 1.0. In: Poole AF, Gill FB (eds) Birds of the world. Cornell Lab of Ornithology, Ithaca, NY, USA. https://doi.org/10.2173/bow.comgal1.01 \u003c/li\u003e\n\u003cli\u003eBattaglia LL, Woodrey MS, Peterson MS, et al (2012) Wetlands of the northern Gulf Coast. In Batzer DP, Baldwin AH (eds) Wetland habitats of North America: ecology \u0026amp; conservation issues. University of California Press, Berkeley, CA, USA, pp 75-88\u003c/li\u003e\n\u003cli\u003eBenoit LK, Askins RA (2002) Relationship between habitat area and the distribution of tidal marsh birds. Wildl Soc Bull 114:314\u0026ndash;323\u003c/li\u003e\n\u003cli\u003eBianchi, TS (2013) Estuarine Chemistry. In: Day JW Jr, Crump BC, Kemp WM, Y\u0026aacute;\u0026ntilde;ez-Arancibia A (eds) Estuarine ecology, Wiley-Blackwell, Hoboken, NJ, USA, pp 39-83\u003c/li\u003e\n\u003cli\u003eChesser RT, Billerman SM, Burns KJ, et al (2024) Check-list of North American birds. American Ornithological Society. https://checklist.americanornithology.org/. Accessed January 2025\u003c/li\u003e\n\u003cli\u003eConner LM, Smith MD, Burger LW (2003) A comparison of distance-based and classification-based analyses of habitat use. Ecology 84:526\u0026ndash;531. https://doi.org/10.1890/0012-9658(2003)084[0526:ACODBA]2.0.CO;2\u003c/li\u003e\n\u003cli\u003eConway CJ (2011) Standardized North American marsh bird monitoring protocol. Waterbirds 34:319\u0026ndash;346.\u003c/li\u003e\n\u003cli\u003eDeepwater Horizon Natural Resource Damage Assessment Trustees (2016) Deepwater Horizon oil spill: final programmatic damage assessment and restoration plan and final programmatic environmental impact statement. National Oceanic and Atmospheric Administration, Silver Spring, MD, USA\u003c/li\u003e\n\u003cli\u003eDoody JP (2004) \u0026lsquo;Coastal squeeze\u0026rsquo;\u0026mdash;an historical perspective. J Coastal Conserv 10:129\u0026ndash;138\u003c/li\u003e\n\u003cli\u003eDuarte A, Adams MJ, Peterson JT (2018) Fitting N-mixture models to count data with unmodeled heterogeneity: Bias, diagnostics, and alternative approaches. Ecol Modell 374:51\u0026ndash;59\u003c/li\u003e\n\u003cli\u003eEleuterius LN (1972) The marshes of Mississippi. Castanea 37:153\u0026ndash;168\u003c/li\u003e\n\u003cli\u003eEnwright NM, Hartley SB, Brasher MG, et al (2014) Delineation of marsh types of the Texas coast from Corpus Christi Bay to the Sabine River in 2010. U.S. Geological Survey Scientific Investigations Report 2014-5110, 18 p. scale 1:400,000. http://dx.doi.org/10.3133/sir20145110\u003c/li\u003e\n\u003cli\u003eEnwright NM, Griffith KT, Osland MJ (2016) Barriers to and opportunities for landward migration of coastal wetlands with sea‐level rise. Front Ecol Environ 14:307\u0026ndash;316. https://doi.org/10.1002/fee.1282\u003c/li\u003e\n\u003cli\u003eErwin RM, Sanders GM, Prosser DJ, Cahoon DR (2006) High tides and rising seas: potential effects on estuarine waterbirds. Stud Avian Biol 32:214\u003c/li\u003e\n\u003cli\u003eESRI (2022) ArcGIS Pro 3.0.3., Environmental Systems Research Institute, Redlands, CA, USA\u003c/li\u003e\n\u003cli\u003eFiske I, Chandler R (2011) Unmarked: an R package for fitting hierarchical models of wildlife occurrence and abundance. J Stat Softw 43:1\u0026ndash;23.\u003c/li\u003e\n\u003cli\u003eGreenberg R (2006) Tidal marshes: home for the few and the highly selected. Stud Avian Biol 32:2\u003c/li\u003e\n\u003cli\u003eGreenberg R, Elphick C, Nordby JC, et al (2006) Flooding and predation: Trade-offs in the nesting ecology of tidal-marsh sparrows. Stud Avian Biol 32:96\u003c/li\u003e\n\u003cli\u003eGreenlaw JS, Shriver WG, Post W (2022) Seaside Sparrow (Ammospiza maritima), version 2.0. In: Rodewald PG, Keeney BK (eds) Birds of the world, Cornell Lab of Ornithology, Ithaca, NY, USA. https://doi. org/10.2173/bow.seaspa02\u003c/li\u003e\n\u003cli\u003eGuzy MJ, Ritchison G (2020) Common Yellowthroat (Geothlypis trichas), version 1.0. In: Rodewald PG (ed) Birds of the world. Cornell Lab of Ornithology, Ithaca, NY, USA. https://doi.org/10.2173/bow.comyel.01\u003c/li\u003e\n\u003cli\u003eHamel PB (1992) Land manager\u0026rsquo;s guide to the birds of the South. The Nature Conservancy, Southeastern Region, Chapel Hill, NC, USA\u003c/li\u003e\n\u003cli\u003eHaney JC, Geiger HJ, Short JW (2014) Bird mortality from the Deepwater Horizon oil spill. II. Carcass sampling and exposure probability in the coastal Gulf of Mexico. Marine Ecology Progress Series 513:239\u0026ndash;252\u003c/li\u003e\n\u003cli\u003eHarrell FE Jr (2023) Hmisc: Harrell, Miscellaneous. R package version 5.0-1\u003c/li\u003e\n\u003cli\u003eHughes RG (2004) Climate change and loss of saltmarshes: consequences for birds. Ibis 146:21\u0026ndash;28. https://doi.org/10.1111/j.1474-919X.2004.00324.x\u003c/li\u003e\n\u003cli\u003eIb\u0026aacute;\u0026ntilde;ez C, Morris JT, Mendelssohn IA, Day JW Jr (2013) Coastal marshes. In: Day JW Jr, Crump BC, Kemp WM, Y\u0026aacute;\u0026ntilde;ez-Arancibia A (eds) Estuarine ecology, Wiley-Blackwell, Hoboken, NJ, USA, pp 1-18\u003c/li\u003e\n\u003cli\u003eIglay RB, Woodrey MS, Evans KO, Rush SA, Feura J (2020) Coastal bird stewardship phase 2 - tidal marsh bird research plan, draft 4. Mississippi State University, Starkville, MS, USA\u003c/li\u003e\n\u003cli\u003eJohnson DH, Gibbs JP, Herzog M, et al (2009) A sampling design framework for monitoring secretive marshbirds. Waterbirds 32:203\u0026ndash;215\u003c/li\u003e\n\u003cli\u003eJohnston A, Fink D, Reynolds MD, et al (2015) Abundance models improve spatial and temporal prioritization of conservation resources. Ecol Appl 25:1749\u0026ndash;1756. https://doi.org/10.1890/14-1826.1\u003c/li\u003e\n\u003cli\u003eJones LR, Johnson SA, Hudson CM, Zollner PA, Swihart RK (2022a) Habitat selection in a recovering bobcat (Lynx rufus) population. PLoS One 17:e0269258\u003c/li\u003e\n\u003cli\u003eJones LR, Swihart RK, Gleich DF, Albers G, Johnson SA, Hudson CM, Zollner PA (2022b) Estimating statewide carrying capacity of bobcats (Lynx rufus) using improved maximum clique algorithms. Landscape Ecol 37:2383\u0026ndash;2397\u003c/li\u003e\n\u003cli\u003eK\u0026eacute;ry M, Royle JA (2017) Modeling abundance with counts of unmarked individuals in closed populations: binomial N-mixture models. In: Applied hierarchical modeling in ecology: Analysis of distribution, abundance and species richness in R and BUGS: Volume 1: Prelude and static models. Academic Press, London, UK, pp 219-311\u003c/li\u003e\n\u003cli\u003eK\u0026eacute;ry M, Royle JA (2021) Applied hierarchical modeling in ecology: Analysis of distribution, abundance and species richness in R and BUGS: Volume 2: Dynamic and advanced models. Academic Press, London, UK, pp 76-82\u003c/li\u003e\n\u003cli\u003eKnape J, Arlt D, Barraquand F, Berg \u0026Aring;, Chevalier M, P\u0026auml;rt T, Ruete A, Zmihorski M (2018) Sensitivity of binomial N‐mixture models to overdispersion: The importance of assessing model fit. Methods Ecol Evol 9:2102\u0026ndash;2114. https://doi.org/10.1111/2041-210X.13062\u003c/li\u003e\n\u003cli\u003eKroodsma DE, Verner J (2020) Marsh Wren (Cistothorus palustris), version 1.0. In: Poole AF (ed) Birds of the world. Cornell Lab of Ornithology, Ithaca, NY, USA. https://doi.org/10.2173/bow.marwre.01 \u003c/li\u003e\n\u003cli\u003eLeggett AH (2014) Distribution, abundance, and habitat associations of breeding marsh birds in Mississippi tidal marsh. Thesis, University of Georgia\u003c/li\u003e\n\u003cli\u003eLehmicke AJJ (2014) Breeding ecology of the Seaside Sparrow (Ammodramus maritimus) in Northern Gulf of Mexico tidal salt marshes. Dissertation, University of Georgia \u003c/li\u003e\n\u003cli\u003eLindenmayer DB, Likens GE (2009) Adaptive monitoring: a new paradigm for long-term research and monitoring. Trends Ecol Evol 24:482\u0026ndash;486\u003c/li\u003e\n\u003cli\u003eLindenmayer DB, Likens GE (2010) The science and application of ecological monitoring. Biol Conserv 143:1317\u0026ndash;1328\u003c/li\u003e\n\u003cli\u003eMay R, Van Dijk J, Wabakken P, Swenson JE, Linnell JD, Zimmermann B, Odden J, Pedersen HC, Andersen R, Landa A (2008) Habitat differentiation within the large‐carnivore community of Norway\u0026rsquo;s multiple‐use landscapes. J Appl Ecol 45:1382\u0026ndash;1391. https://doi.org/10.1111/j.1365-2664.2008.01527.x\u003c/li\u003e\n\u003cli\u003eMouton EC, Felder DL (1996) Burrow Distributions and Population Estimates for the Fiddler Crabs Uca spinicarpa and Uca longisignalis in a Gulf of Mexico Salt Marsh. Estuaries 19:51. https://doi.org/10.2307/1352651\u003c/li\u003e\n\u003cli\u003eNASEM (National Academies of Science, Engineering, and Medicine) (2017) Effective monitoring to evaluate ecological restoration in the Gulf of Mexico. National Academies Press\u003c/li\u003e\n\u003cli\u003eNational Commission on the BP Deep Ocean Horizon Oil Spill and Offshore Drilling (2011) Deep water: The Gulf oil disaster and the future of offshore drilling. Report to the President. www.oilspillcommission.gov. Accessed May 2021\u003c/li\u003e\n\u003cli\u003eNOAA (National Oceanic and Atmospheric Administration) (2010) Coastal Change Analysis Program (C-CAP) regional land cover. NOAA Office for Coastal Management. Charleston, SC, USA. www.coast.noaa.gov/ccapftp. Accessed April 2021\u003c/li\u003e\n\u003cli\u003eNOAA (National Oceanic and Atmospheric Administration) (2017) Coastal Change Analysis Program (C-CAP) derived land cover - BETA. NOAA Office for Coastal Management. Charleston, SC, USA. www.coast.noaa.gov/ccapftp. Accessed April 2021\u003c/li\u003e\n\u003cli\u003eNorris K (2004) Managing threatened species: the ecological toolbox, evolutionary theory and declining‐population paradigm. J Appl Ecol 41:413\u0026ndash;426. https://doi.org/10.1111/j.0021-8901.2004.00910.x\u003c/li\u003e\n\u003cli\u003eNovak JN, Gaines KF, Cumbee Jr JC, Mills GL, Rodriguez-Navarro A, Romanek CS (2006) The Clapper Rail as an indicator species of estuarine marsh health. In: Greenberg R, Maldonado JE, Droege S, MacDonald MV (eds) Terrestrial vertebrates of tidal marshes: evolution, ecology, and conservation. Stud Avian Biol 32:270-281\u003c/li\u003e\n\u003cli\u003eOdum WE (1988) Comparative ecology of tidal freshwater and salt marshes. Annu. Rev. Ecol. Evol. Syst. 19:147\u0026ndash;176\u003c/li\u003e\n\u003cli\u003ePendleton EA, Barras JA, Williams SJ, Twichell DC (2010) Coastal vulnerability assessment of the Northern Gulf of Mexico to sea-level rise and coastal change. US Geological Survey Open-File Report 2010\u0026ndash;1146. U.S. Department of the Interior, U.S. Geological Survey, Reston, VI, USA\u003c/li\u003e\n\u003cli\u003ePielou EC (1969) An introduction to mathematical ecology. Wiley-Interscience. New York, NY, USA \u003c/li\u003e\n\u003cli\u003ePoole AF, Lowther PE, Gibbs JP, Reid FA, and Melvin SM (2020) Least bittern (Ixobrychus exilis), version 1.0. In: Poole AF (ed) Birds of the world. Cornell Lab of Ornithology, Ithaca, NY, USA. https://doi.org/10.2173/bow.leabit.01\u003c/li\u003e\n\u003cli\u003ePost W (1974) Functional Analysis of Space‐Related Behavior in the Seaside Sparrow. Ecology 55:564\u0026ndash;575. https://doi.org/10.2307/1935147\u003c/li\u003e\n\u003cli\u003eR Core Team (2021) R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria\u003c/li\u003e\n\u003cli\u003eRobel RJ, Briggs JN, Dayton AD, Hulbert LC (1970) Relationships between visual obstruction measurements and weight of grassland vegetation. J Range Manage 23:295\u0026ndash;297\u003c/li\u003e\n\u003cli\u003eRoyle JA (2004) Generalized estimators of avian abundance from count survey data. Anim Biodivers Conserv 27:375\u0026ndash;386\u003c/li\u003e\n\u003cli\u003eRush SA, Soehren EC, Woodrey MS, Graydon CL, Cooper RJ (2009) Occupancy of select marsh birds within northern Gulf of Mexico tidal marsh: current estimates and projected change. Wetlands 29:798\u0026ndash;808\u003c/li\u003e\n\u003cli\u003eRush SA, Mordecai R, Woodrey MS, Cooper RJ (2010a) Prey and habitat influences the movement of clapper rails in Northern Gulf coast estuaries. Waterbirds 33:389\u0026ndash;396\u003c/li\u003e\n\u003cli\u003eRush SA, Woodrey MS, Cooper RJ (2010b) Variation in the nesting habits of clapper rails in tidal marshes of the northern Gulf of Mexico. The Condor 112:356\u0026ndash;362\u003c/li\u003e\n\u003cli\u003eRush SA, Rodgers III J, Soehren EC, Trent JA (2019) Spatial and temporal changes in emergent marsh and associated marsh birds of the lower Mobile-Tensaw River Delta in Alabama, USA. Wetlands 39:1189\u0026ndash;1201\u003c/li\u003e\n\u003cli\u003eRush SA, Gaines KF, Eddleman WR, Conway CJ (2020) Clapper Rail (Rallus crepitans), version 1.0. In: Rodewald PG (ed) Birds of the world, Cornell Lab of Ornithology, Ithaca, NY, USA. https://doi.org/10.2173/bow.clarai11.01\u003c/li\u003e\n\u003cli\u003eSHARP (Saltmarsh Habitat and Avian Research Program) (2015). Vegetation Survey Protocol-2015. https://www.tidalmarshbirds.org. Accessed March 2021\u003c/li\u003e\n\u003cli\u003eSHARP (Saltmarsh Habitat and Avian Research Program) (2022). Saltmarsh habitat avian research program. https://www.tidalmarshbirds.org. Accessed March 2021\u003c/li\u003e\n\u003cli\u003eSchelske CL, Odum EP (1961) Mechanisms maintaining high productivity in Georgia estuaries. Proceedings of the Gulf and Caribbean Fisheries Institute 14:75-80\u003c/li\u003e\n\u003cli\u003eSnedden, G. A., Cable JE, Bjӧrn K (2013) Estuarine Geomorphology and Coastal Hydrology. In: Day JW Jr, Crump BC, Kemp WM, Y\u0026aacute;\u0026ntilde;ez-Arancibia A (eds) Estuarine ecology. Wiley-Blackwell, Hoboken, NJ, USA, pp 19-38\u003c/li\u003e\n\u003cli\u003eSpautz H, Nur N, Stralberg D, Chan Y (2006) Multiple-scale habitat relationships of tidal-marsh breeding birds in the San Francisco Bay estuary. Stud Avian Biol 32:247\u003c/li\u003e\n\u003cli\u003eStout JP (1984) The ecology of irregularly flooded salt marshes of the northeastern Gulf of Mexico: a community profile. National Coastal Ecosystems Team, Division of Biological Services, Research and Development, U.S. Department of the Interior, Fish and Wildlife Service, Washington, DC, USA\u003c/li\u003e\n\u003cli\u003eTorio DD, Chmura GL (2015) Impacts of sea level rise on marsh as fish habitat. Estuaries Coasts 38:1288\u0026ndash;1303\u003c/li\u003e\n\u003cli\u003eUSFWS (U.S. Fish and Wildlife Service), USGS (U.S. Geological Service) (2006) Strategic habitat conservation: final report of the National Ecological Assessment Team. http://www.fws.gov/science/doc/SHC_FinalRpt.pdf. Accessed March 2021\u003c/li\u003e\n\u003cli\u003eVeech JA, Ott JR, Troy JR (2016) Intrinsic heterogeneity in detection probability and its effect on \u003cem\u003eN \u003c/em\u003e‐mixture models. Methods Ecol Evol 7:1019\u0026ndash;1028. https://doi.org/0.1111/2041-210X.12566\u003c/li\u003e\n\u003cli\u003eWeitzel SL, Feura JM, Iglay RB, Evans KO, Rush SA, Woodrey MS (2021) Distribution, abundance, and vegetation associations of birds in Mississippi tidal marshes during the non‐breeding season. J Field Ornith 92:231\u0026ndash;245. https://doi.org/0.1111/jofo.12375\u003c/li\u003e\n\u003cli\u003eWest RL, Hess GK (2020) Purple Gallinule (Porphyrio martinica), version 1.0. In: Poole A, Gill FB (eds) Birds of the world. Cornell Lab of Ornithology, Ithaca, NY, USA. https://doi. org/10.2173/bow. purgal2\u003c/li\u003e\n\u003cli\u003eWhite D (2007) Spatial grid networks. U.S. Environmental Protection Agency, Western Ecology Division, Corvallis, OR, USA. http://www.epa.gov/wed/pages/staff/white/getgrid.htm. Accessed 31 July 2018\u003c/li\u003e\n\u003cli\u003eWieland RG (2007) Habitat types and associated ecological communities of the Grand Bay National Estuarine Research Reserve. In: Peterson MS, Waggy GL, Woodrey MS (eds) Grand Bay National Estuarine Research Reserve: an ecological characterization. Grand Bay National Estuarine Research Reserve, Moss Point, MS, USA, pp 104-147\u003c/li\u003e\n\u003cli\u003eWiest WA, Correll MD, Olsen BJ, Elphick CS, Hodgman TP, Curson DR, Shriver WG (2016) Population estimates for tidal marsh birds of high conservation concern in the northeastern USA from a design-based survey. The Condor: Ornithol Appl 118:274\u0026ndash;288\u003c/li\u003e\n\u003cli\u003eWigand C, Roman CT (2012) North Atlantic Coastal Tidal Wetlands. In: Batzer DP, Baldwin AH (eds) Wetland Habitats of North America. University of California Press, Berkeley, CA, USA, pp 13\u0026ndash;28\u003c/li\u003e\n\u003cli\u003eWoodrey MS, Rush SA, Cherry JA, Nuse BL, Cooper RJ, Lehmicke AJJ (2012) Understanding the potential impacts of global climate change on marsh birds in the Gulf of Mexico region. Wetlands 32:35\u0026ndash;49\u003c/li\u003e\n\u003cli\u003eWoodrey MS, Fournier AM, Cooper RJ (2019) GoMAMN strategic bird monitoring guidelines: marsh birds. In: Wilson RR, Fournier AMV, Gleason JS, Lyons JE, Woodrey MS (eds) Strategic bird monitoring guidelines for the northern Gulf of Mexico. Mississippi Agricultural and Forestry Experiment Station Research Bulletin 1228, Mississippi State University, USA, pp 71\u0026ndash;96\u003c/li\u003e\n\u003cli\u003eYasukawa K, Searcy WA (2020) Red-winged blackbird (Agelaius phoeniceus), version 1.0. In: Rodewald RG (ed) Birds of the world. Cornell Lab of Ornithology, Ithaca, NY, USA. https://doi.org/10.2173/bow.rewbla.01\u003c/li\u003e\n\u003cli\u003eYip DA, Bayne EM, S\u0026oacute;lymos P, Campbell J, Proppe D (2017) Sound attenuation in forest and roadside environments: Implications for avian point-count surveys. The Condor: Ornithol Appl 119:73\u0026ndash;84\u003c/li\u003e\n\u003cli\u003eZuur AF, Ieno EN, Walker N, Saveliev AA, Smith GM (2009) Mixed effects models and extensions in ecology with R. Springer, New York, NY, USA. https://doi.org/10.1007/978-0-387-87458-6\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":true,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"wetlands","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"wela","sideBox":"Learn more about [Wetlands](https://www.springer.com/journal/13157)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/wela/default.aspx","title":"Wetlands","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"breeding birds, Gulf of Mexico, landscape associations, monitoring, multi-scale, population estimates, tidal marsh","lastPublishedDoi":"10.21203/rs.3.rs-6214450/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6214450/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eTidal marsh birds across the northern Gulf of Mexico are subject to numerous natural and anthropogenic disturbances. Despite many species of concern inhabiting the area, baseline population estimates are lacking which prohibit effective tidal marsh conservation planning. Thus, we generated population estimates and determined landscape associations at local and broad scales for Clapper Rails (\u003cem\u003eRallus crepitans\u003c/em\u003e), gallinules (\u003cem\u003eGallinula galeata\u003c/em\u003e and \u003cem\u003ePorphyrio martinicus\u003c/em\u003e), Least Bitterns (\u003cem\u003eIxobrychus exilis\u003c/em\u003e), Marsh Wrens (\u003cem\u003eCistothorus palustris\u003c/em\u003e), Seaside Sparrows (\u003cem\u003eAmmospiza maritimus\u003c/em\u003e), Red-winged Blackbirds (\u003cem\u003eAgelaius phoeniceus\u003c/em\u003e), and Common Yellowthroats (\u003cem\u003eGeothlypis trichas\u003c/em\u003e) across the Mississippi Gulf Coast. The abundance of most tidal marsh birds in our study was inversely related to landscape heterogeneity, vegetation height, and further proximity to open water. Common Yellowthroat abundance was positively influenced by proximity to uplands and palustrine wetlands, but Seaside Sparrows were negatively impacted. Seaside Sparrow and Clapper Rail abundance was positively associated with proximity to estuarine marsh and percent salt-tolerant vegetation, unlike Common Yellowthroats and gallinules with contrasting relationships. Managers should consider these species-specific differences, as population sizes and landscape relationships varied within the tidal marsh community. Overall, large expanses of tidal marsh across the salinity gradient will promote greater abundance for the tidal marsh bird community as a whole, but local vegetation structure, fine-scale salinity data, and adjacent land cover types will need to be considered for specialist species like Seaside Sparrows. Our robust monitoring and modelling framework can be easily expanded across the Gulf of Mexico, and we recommend this idea be considered by the conservation community across the region.\u003c/p\u003e","manuscriptTitle":"Informing conservation planning with population estimates and divergent landscape associations of tidal marsh birds","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-03-27 08:58:23","doi":"10.21203/rs.3.rs-6214450/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewerAgreed","content":"","date":"2025-03-20T12:43:58+00:00","index":0,"fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-03-18T15:59:41+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"Wetlands","date":"2025-03-13T16:05:36+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-03-13T01:51:25+00:00","index":"","fulltext":""},{"type":"submitted","content":"Wetlands","date":"2025-03-12T15:04:51+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"wetlands","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"wela","sideBox":"Learn more about [Wetlands](https://www.springer.com/journal/13157)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/wela/default.aspx","title":"Wetlands","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"9182d876-331f-4885-a787-cfe8a382b3b6","owner":[],"postedDate":"March 27th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-10-20T16:07:21+00:00","versionOfRecord":{"articleIdentity":"rs-6214450","link":"https://doi.org/10.1007/s13157-025-01987-9","journal":{"identity":"wetlands","isVorOnly":false,"title":"Wetlands"},"publishedOn":"2025-10-16 15:58:13","publishedOnDateReadable":"October 16th, 2025"},"versionCreatedAt":"2025-03-27 08:58:23","video":"","vorDoi":"10.1007/s13157-025-01987-9","vorDoiUrl":"https://doi.org/10.1007/s13157-025-01987-9","workflowStages":[]},"version":"v1","identity":"rs-6214450","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6214450","identity":"rs-6214450","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}