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Assessment of Bird Response to the Migratory Bird Habitat Initiative Using Weather-Surveillance Radar
Mason L. Sieges, Jaclyn A. Smolinsky, Michael J. Baldwin, Wylie C. Barrow, Jr., Lori A. Randall, and Jeffrey J. Buler

Southeastern Naturalist, Volume 13, Issue 1 (2014): G36–G65

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Southeastern Naturalist M.L. Sieges, J.A. Smolinsky, M.J. Baldwin, W.C. Barrow, Jr., L.A. Randall, and J.J. Buler 2014 Vol. 13, No. 1 G36 2014 Assessment of Bird Response to the Migratory Bird Habitat Initiative Using Weather-Surveillance Radar Mason L. Sieges1, Jaclyn A. Smolinsky1, Michael J. Baldwin2, Wylie C. Barrow, Jr.2, Lori A. Randall2, and Jeffrey J. Buler1,* Abstract - In response to the Deepwater Horizon oil spill in spring 2010, the Natural Resources Conservation Service implemented the Migratory Bird Habitat Initiative (MBHI) to provide temporary wetland habitat for migrating and wintering waterfowl, shorebirds, and other birds along the northern Gulf of Mexico via managed flooding of agricultural lands. We used weather-surveillance radar to conduct broad regional assessments of bird response to MBHI activities within the Mississippi Alluvial Valley and the West Gulf Coastal Plain. Across both regions, birds responded positively to MBHI management by exhibiting greater relative bird densities within sites relative to pre-management conditions in prior years and relative to surrounding non-flooded agricultural lands. Bird density at MBHI sites was generally greatest during winter for both regions. Unusually high flooding in the years prior to implementation of the MBHI confounded detection of overall changes in remotely sensed soil wetness across sites. The magnitude of bird response at MBHI sites compared to prior years and to non-flooded agricultural lands was generally related to the surrounding landscape context: proximity to areas of high bird density, amount of forested wetlands, emergent marsh, non-flooded agriculture, or permanent open water. However, these relationships varied in strength and direction between regions and seasons, a finding which we attribute to differences in seasonal bird composition and broad regional differences in landscape configuration and composition. We detected greater increases in relative bird use at sites in closer proximity to areas of high bird density during winter in both regions. Additionally, bird density was greater during winter at sites with more emergent marsh in the surrounding landscape. Thus, bird use of managed wetlands could be maximized by enrolling lands located near areas of known bird concentration and within a mosaic of existing wetlands. Weather-radar observations provide strong evidence that MBHI sites located inland from coastal wetlands impacted by the oil spill provided wetland habitat used by a variety of birds. Introduction The northern Gulf Coast is home to diverse wetlands arrayed along 75,000 km of shoreline that include habitat for a wide variety of resident and migratory waterbirds (Helmers 1992, Mikuska et al. 1998, Musumeche et al. 2002). These wetlands have been significantly degraded by human-induced landscape alterations (Britsch and Dunbar 1993, Ellis and Dean 2012, Nestlerode et al. 2009), sea-level rise associated with climate change (Hoozemans et al. 1993), powerful storms (Barras 2006, Lopez 2009), and the Deepwater Horizon oil spill in April 2010, the largest spill to date off the Gulf Coast (Copeland 2010). SOUTHEASTERN NATURALIST Gulf of Mexico Natural History and Oil Spill Impacts Special Series 1Department of Entomology and Wildlife Ecology, University of Delaware, 531 South College Avenue, Newark, DE 19716. 2US Geological Survey, National Wetlands Research Center, 700 Cajundome Boulevard, Lafayette, LA 70506. *Corresponding author - jbuler@udel.edu. Manuscript Editor: Frank R. Moore 13(1):G36–G65 Southeastern Naturalist G37 M.L. Sieges, J.A. Smolinsky, M.J. Baldwin, W.C. Barrow, Jr., L.A. Randall, and J.J. Buler 2014 Vol. 13, No. 1 In response to this event, the Natural Resources Conservation Service (NRCS) implemented the Migratory Bird Habitat Initiative (MBHI) to provide migrating and wintering waterfowl, shorebirds, and other birds with alternative habitats to compensate for loss of coastal wetlands impacted by the oil spill. Wetland habitat was created through the MBHI program by paying private landowners to flood existing farmed wetlands, previously converted croplands, and other lands that had not been actively flooded during the winter months for the previous three years. Numerous bird species use flooded agricultural lands and adjacent areas for daytime roosting and foraging along the Gulf Coast (Floyd 2000, Huner 1995, Musumeche et al. 2002, Remsen et al. 1991). The NRCS identified the Mississippi Alluvial Valley (MAV) and West Gulf Coastal Plain (WGCP) ecoregions as program priority areas because of their adjacency to oil-spill–impacted-wetlands. In the fall of 2010, MBHI activities commenced on private agricultural or other lands already enrolled in existing Farm Bill Programs: Wetlands Reserve Program (WRP), Environmental Quality Incentives Program (EQIP), and Wildlife Habitat Incentive Program (WHIP). Program activities continued through the winter for all MAV sites and through the spring of 2011 (or longer for some sites in Louisiana with multi-year contracts) for sites within the WGCP. Approximately 188,375 hectares were enrolled into the MBHI within the MAV and WGCP across five states (Texas, Louisiana, Arkansas, Missouri, and Missippi; USDA NRCS 2012). Water levels at MBHI sites were managed for shallow water, mudflat, and sandflat habitats to create or enhance habitat for shorebirds and waterfowl. According to the NRCS practice standard for shallow water development and management (code 646; USDA NRCS 2010), flooding 0–10 cm (0–4 in ) from July to October provides habitat for shorebirds, and water depths 15–20 cm (6–10 in ) from October to March benefit waterfowl. Although water management protocols at sites within each state were intended to be identical, variability in actual water management, site characteristics and location, and features of the surrounding landscape may have resulted in differential bird use among sites. For example, in the Central Valley of California, wintering waterfowl use of managed wetlands is greater at sites with greater soil wetness (i.e., extent of managed flooding), with fewer wetlands in the surrounding landscape, and with greater proximity to flooded rice fields where waterfowl typically forage at night (Buler et al. 2012a). The amount and type of agricultural fields in the surrounding landscape may attract some species while deterring others that are more sensitive to human disturbance and development (Czech and Parsons 2002, Niemuth et al. 2006). The amount of open water near managed lands (Fairbairn and Dinsmore 2001, Manley et al. 2005) may also play a role in how birds use wetlands for roosting and feeding. Waterfowl often react to avian and terrestrial predators by moving to open water and grouping together in refugia (Tamisier 1976). Cox and Afton (1997) found that female Anas acuta L. (Northern Pintail), regularly use pools of open water on hunting refuges during the fall hunting season in southwestern Louisiana. Based on refuging theory, MBHI sites located near refuges with high bird concentrations may be used more heavily than sites far from refuges (Cox and Afton 1996, Link et al. 2011). Southeastern Naturalist M.L. Sieges, J.A. Smolinsky, M.J. Baldwin, W.C. Barrow, Jr., L.A. Randall, and J.J. Buler 2014 Vol. 13, No. 1 G38 Due to the rapid implementation of the MBHI program, there is little data regarding bird use prior to management at most sites. This lack of baseline information limits assessment of the program’s efficacy through traditional field-survey methods. Additionally, a comprehensive assessment of bird response among the numerous and widespread sites in both regions through traditional field surveys is not financially or logistically feasible. Instead, remotely sensed weather-surveillance radar observations of bird activity can provide a more comprehensive assessment of bird use at numerous sites and, because they are archived, provide observations of bird use prior to enrollment in the MBHI program. The current national network of weathersurveillance radars (model WSR-88D, commonly referred to as NEXRAD) is an important tool used to study a variety of bird movements across the United States (Bonter et al. 2007; Diehl et al. 2003; Gauthreaux and Belser 1998, 2003; Kelly et al. 2012). NEXRAD can be used to measure bird densities and map their distributions on the ground as birds take flight en masse from terrestrial habitats at the onset of highly synchronized broad-scale movements, such as nocturnal feeding flights of wintering waterfowl and migratory flights of land birds (Buler and Diehl 2009, Buler and Moore 2011, Buler et al. 2012a). Specifically, along the Gulf Coast during the winter, large groups of waterfowl and other species regularly undertake flights between roosting sites—usually wetlands and bodies of water—and feeding habitats such as agricultural fields (Buler et al. 2012a, Paulus 1988, Randall et al. 2011). These highly-synchronized movements tend to occur near sunrise and sunset and are closely related to sun elevation (Baldassarre and Bolen 1984, Cox and Afton 1996, Ely 1992, Raveling et al. 1972). Similarly, many birds, including waterfowl, shorebirds, and land birds, initiate nocturnal migratory flights shortly after sunset (Akesson et al. 1996, Bonter et al. 2009, Diehl et al. 2003, Gauthreaux and Belser 2003, Hebrard 1971). Methods Study area MBHI sites were located within several states of the MAV (Missouri, Arkansas, and Mississippi) and the WGCP (Louisiana and Texas) (Fig. 1). The predominant agricultural land-uses are soybean and rice fields in the MAV and aquaculture (ricecultivation and crawfish farming), pastures, hayfields, and idle/fallow cropland in the WGCP region (USDA NASS CDL 2010). Rice farming is ideal for integrating an established agricultural practice with the goal of waterbird conservation because the water-control infrastructure necessary for farming can be used to manipulate habitat to benefit waterbirds (Elphick 2000, Huner et al. 2002, Norling et al. 2012). Six NEXRAD stations are located within the study area and potentially provide surveillance of MBHI sites: Lake Charles, LA (KLCH); Houston, TX (KHGX); Little Rock, AR (KLZK); Memphis, TN (KNQA); Paducah, KY (KPAH); and Ft. Polk, LA (KPOE). We did not consider data from KPOE because it is not archived in its native Level II format. We obtained information about MBHI tract boundaries and management activities from state NRCS offices. We excluded from analysis individual sites that were smaller than 0.5 ha in area. Only Arkansas sites were within the effective radar detection range for radars within the MAV; therefore sites in Mississippi and Missouri and all data from KPAH were excluded from analysis. Southeastern Naturalist G39 M.L. Sieges, J.A. Smolinsky, M.J. Baldwin, W.C. Barrow, Jr., L.A. Randall, and J.J. Buler 2014 Vol. 13, No. 1 MBHI sites were under varying degrees of active moist soil management, depending on the timing and intensity of water-level manipulation. In Texas and Arkansas, fields were flooded to a water depth of 5–46 cm (2–18 in). Based on the timing of management in Texas and Arkansas, we defined our seasons for both regions as fall (1 October–31 October), winter (1 November–28 February), and, for Figure 1. Locations of MBHI sites (black dots) within the effective observation areas (dark grey) of four weather surveillance radars (labeled by name) within the Mississippi Alluvial Valley and West Gulf Coastal Plain regions of the southern US. The light grey area denotes counties of states included in the MBHI program. Southeastern Naturalist M.L. Sieges, J.A. Smolinsky, M.J. Baldwin, W.C. Barrow, Jr., L.A. Randall, and J.J. Buler 2014 Vol. 13, No. 1 G40 WGCP only, spring (1 March–31 March). Louisiana offered a variety of habitats that were managed to benefit different groups of waterbirds. The 4 management settings used were: mudflats that were disked or rolled and flooded to a maximum of 5 cm (2 in) to benefit early migrating waterfowl and shorebirds, food/cover habitat where the vegetation was left standing and flooded to a depth of 15–25 cm (6–10 in) to provide forage and sanctuary for wintering waterfowl, crawfish ponds to provide invertebrate prey for waterbirds through the winter to mid-summer, and an extension of either the mudflat or food/cover practice type. Additionally, Louisiana altered the timing of management among these types. Therefore, we limited analysis of Louisiana sites to those that most closely matched the timing and type of management at Texas sites for the WGCP region. In fall, we only included Louisiana sites with managed mudflats or active flooding associated with food/cover habitat from 1 October to 31 October. In winter and spring, we only used Louisiana sites with active flooding associated with food/cover habitat. Winter management at Louisiana sites occurred during a narrower timeframe than at Texas sites (15 November to 30 January). In the WGCP, we analyzed sites totaling 14,177 ha in the fall (7732 ha in TX and 6445 ha in LA), 12,141 ha in the winter (6039 ha in TX and 6102 ha in LA), and 6924 ha in the spring (6400 ha in TX and 524 ha in LA). In the MAV, we analyzed sites totaling 2575 ha and 2519 ha for fall and winter, respectively. Variability in the area analyzed is due to differences in the amount of area enrolled between seasons and differences in the effective detection range of the radar among sampling days. Overall, we sampled approximately 10% of the total area enrolled in MBHI within Arkansas (MAV) and 15% of enrolled area in Texas and Louisiana (WGCP). Weather-surveillance radar data We obtained radar data collected during time periods associated with migrating and wintering bird movements from 15 August through 31 May for the years 2008– 2011 at KLCH, KHGX, KZLK, and KNQA from the National Climatic Data Center data archive (http://www.ncdc.noaa.gov/nexradinv/). Radars measure reflectivity factor (Z) in the form of returned radiation (Crum and Alberty 1993) within sample volumes having dimensions of 250 m long by 0.5º diameter. The density of birds on the ground is positively correlated to radar reflectivity at the onset of flight exodus (Buler and Diehl 2009, Buler et al. 2012a). We used radar data from nights with no discernible influence from precipitation or ground returns from extreme radar-beam refraction. Additionally, we excluded data from individual sample volumes subject to persistent ground clutter and beam blockage. We flattened radar-sample volumes into their two-dimensional polar boundaries (250 m deep and 0.5º wide) to produce sample polygons for overlaying onto land-cover maps using GIS. These sample polygons represent the elementary measurement resolution of radar reflectivity. We interpolated reflectivity measures to an elevation angle of 5.5° below horizon sensu Buler et al. (2012a), to reduce temporal sampling error and bias (Buler and Diehl 2009). Buler et al. (2012a) found that this is the optimal sun angle for quantifying ground densities of waterfowl, and it is temporally close to the onset Southeastern Naturalist G41 M.L. Sieges, J.A. Smolinsky, M.J. Baldwin, W.C. Barrow, Jr., L.A. Randall, and J.J. Buler 2014 Vol. 13, No. 1 of nocturnal feeding flights of wintering waterfowl (Baldassarre and Bolen 1984, Cox and Afton 1996, Miller 1985, Randall et al. 2011, Tamisier 1976) and nocturnal flights of migrating birds (Akesson et al. 1996, Gauthreaux 1971, Hebrard 1971). We adjusted reflectivity measures to reduce range-dependent measurement bias caused by the systematic change in how the vertical distribution of birds in the airspace is sampled as the beam spreads with range from the radar using algorithms implemented in the software program BIRDS as described and developed in Buler et al. (2012a). Soil-wetness data We used remotely sensed Landsat Thematic Mapper (TM) data to quantify the extent of flooding during the MBHI management year and two previous years via a soil-wetness index. The extent of actual flooding is often dependent on water supplies and landowner compliance (Huner et al. 2002, L.A. Randall, pers. comm.). We did not measure water depth at MBHI sites. Rather, we used TM , which can detect soil moisture and the extent of the surface water remotely (Alsdorf et al. 2007, Baker et al. 2007, Rodgers and Smith 1997). We screened and downloaded all available TM data to obtain as many cloud-free images per season as possible from the USGS (http://glovis.usgs.gov/). We calculated the mean soil-wetness index via the tasseled cap transformation of Huang et al. (2002) for TM 7 data and Crist (1985) for TM 5 data. TM data have a spatial resolution of 30 m x 30 m. Increasing values indicate increasing soil wetness. Based on confirmation with visual inspection of imagery, we defined open surface-water condition (flooded soil) as areas with index values greater than -0.05 (Fig. 2). We used this threshold to determine the extent of flooding within MBHI-enrolled areas. We also determined the change in soil wetness from baseline years (2008–2009 and 2009–2010) to the management year (2010–2011) in fall and winter. During the spring of 2011, all TM images in the KHGX and KLCH radar ranges were obscured by clouds; therefore, we could not compare site soil-wetness during spring management to the baseline years. Landscape composition and position data We quantified the amount of four land-cover types surrounding individual radar sample polygons as measures of landscape composition. We calculated the percent cover of agricultural land, emergent marsh, permanent open water, and forested wetlands in the surrounding landscape at multiple scales using the 30-m resolution 2006 National Land-cover Database produced by the USGS Multi-Resolution Land Characteristics Consortium (http://www.mrlc.gov/). We determined the percentage of flooded vs. non-flooded agricultural land using the soil-wetness index derived from TM imagery. We classified agricultural fields with a maximum seasonal wetness-index value less than -0.05 as non-flooded, and fields with values greater than -0.05 as flooded. We determined a single-characteristic scale at which birds responded most strongly (i.e., strongest correlation) to each land-cover type in the landscape (sensu Holland et al. 2004) by assessing the correlations between mean radar reflectivity of MBHI site polygons and the proportion of land cover surrounding polygons among landscapes within a 500-m–4500-m radius at intervals of 500 m. We analyzed data from each radar site separately by season. We randomly Southeastern Naturalist M.L. Sieges, J.A. Smolinsky, M.J. Baldwin, W.C. Barrow, Jr., L.A. Randall, and J.J. Buler 2014 Vol. 13, No. 1 G42 selected 25 samples of 20 polygons separated by at least 4 km for testing. We averaged Spearman rank correlation coefficients among the set of samples to assess correlations. We did not assess correlations for KNQA because of the scarcity of MBHI-enrolled areas. We used the single-characteristic scale for each land-cover type by season and radar site for further analyses. We calculated the mean distance of each sample polygon to the nearest polygon having a seasonal mean reflectivity during baseline years above the 90th percentile as a measure of its relationship within the landscape to an area of high bird density. We used the area-weighted mean reflectivity of all sample polygons to determine Figure 2. Mean soil-wetness index data for 12 MBHI sites (black outlines) located in Texas derived from TM data. Three TM images show temporal variation in wetness data. Sites are completely flooded in the October 2010 image in accordance w ith MBHI management. Corresponding mean wetness-index values are plotted for the entire study period illustrating the fall–winter–spring flooding regime on the 12 MBHI sites. Shaded bars distinguish the periods of active management. Southeastern Naturalist G43 M.L. Sieges, J.A. Smolinsky, M.J. Baldwin, W.C. Barrow, Jr., L.A. Randall, and J.J. Buler 2014 Vol. 13, No. 1 the value of the 90th percentile of reflectivity by radar and season. This approach effectively identified areas with the highest bird density (top decile) that occurred within each radar-observed area. Some of the identified sites were previously documented bird concentration areas, such as Lacassine National Wildlife Refuge (NWR) and Cameron Prairie NWR in Louisiana (Link et al. 2011), where wintering waterfowl congregate. Data analyses We standardized reflectivity measures in order to control for annual fluctuations in overall bird populations that could influence absolute reflectivity measures. Because we were also interested in comparing relative bird density on managed agricultural lands (flooded) to unmanaged agricultural lands (unflooded), we standardized reflectivity values by dividing the seasonal mean reflectivity of a given sample polygon by the area-weighted seasonal mean reflectivity of all radar-sample polygons dominated (>75% of area) by non-flooded agricultural lands for each radar, season, and year combination. We excluded non-flooded agricultural areas ≤1 km from flooded agriculture to minimize potential contamination from birds using nearby flooded fields at the time of sampling. Thus, a standardized reflectivity value of 1 equals the mean relative bird density of non-flooded agricultural fields for a given season, year, and region. For spring 2011, when images were unusable due to cloud contamination, we standardized reflectivity values by dividing the mean reflectivity within a given sample polygon by the area-weighted seasonal mean reflectivity of all radar-sample polygons dominated (>75% of area) by agriculture. For MBHI-managed areas, we calculated the area-weighted mean standardized reflectivity of the portion of sample polygons within site boundaries. We used this standardized reflectivity as an indicator of bird response to MBHI management and the response variable for modeling bird use of MBHI areas within the management year. We also examined bird response to MBHI activities by comparing bird density in the two years prior to management (2008 and 2009) to bird density during the active management year (2010). To do this, we divided the standardized reflectivity at MBHI areas during the management year by the standardized reflectivity at areas across the prior years by season and region. We used this ratio as a second indicator of bird response to MBHI management and the response variable for modeling bird use of MBHI areas between years. A ratio value greater than 1 indicated that bird density was greater during the management year. Additionally, using this ratio helped to control for perennial contamination in the airspace from birds taking flight from the surrounding landscape (Buler et al. 2012b). To understand how management practices influenced our total assessed area, we also calculated the proportion of MBHI areas that showed increases in mean wetness, mean reflectivity during the management year, and mean reflectivity relative to prior years. Modeling bird response We used linear regression modeling with an information-theoretic approach to determine the relative importance of variables in explaining variation in reflectivity among areas (Burnham and Anderson 2002). To minimize spatial autocorrelation Southeastern Naturalist M.L. Sieges, J.A. Smolinsky, M.J. Baldwin, W.C. Barrow, Jr., L.A. Randall, and J.J. Buler 2014 Vol. 13, No. 1 G44 while maintaining adequate sample sizes, we sampled 25 subsets of 20 radarsample volumes spaced at least 4 km apart. We averaged results across sample runs when assessing models. However, as reported earlier, we were unable to model bird response for the KNQA radar. We also did not model bird response during spring for the WGCP because we had no suitable TM imagery to determine soil wetness. We modeled two response variables: standard reflectivity during the management year and the ratio of reflectivity relative to prior years. Explanatory variables included a single soil-wetness variable (either soil wetness during the management year or the change in site wetness from prior years) and several landscape variables: 1) proximity to high bird-density area; 2) amount of forested wetlands in the surrounding landscape; 3) amount of non-flooded agricultural fields in the surrounding landscape; 4) amount of permanent open water in the surrounding landscape; and 5) for WCGP radars, amount of emergent marsh in the surrounding landscape (Table 1). We considered all possible combinations of models with main effects: 63 for WGCP radars and 31 for KLZK. We did not include amount of emergent marsh in the landscape as a covariate for the KLZK because it was ≤1% of the landscape. Data were log-transformed when necessary to improve normalcy of their distributions. We used Akaike’s information criterion, adjusted for small sample sizes, and Akaike weights to determine support for models (Burnham and Anderson 2002). After summing the weights across all models to estimate the relative importance of the variables of interest, we calculated the mean standardized regression coefficient for all models to determine the direction and importance of effect sizes. We estimated precision using an unconditional variance estimator that incorporated model selection uncertainty (Burnham and Anderson 2002) and considered the effect of Table 1. Summary statistics of landscape variables used for modeling bird response among Migratory Bird Habitat Initiative sites by radar and season. Sample sizes reported in Table 3. KLCH KHGX KLZK Variable Mean (range) Mean (range) Mean (range) Fall Proportion of cover type within a 4.5-km radius Permanent open water 0.02 (0.00–0.23) 0.03 (0.00–0.48) 0.05 (0.01–0.16) Forested wetland 0.06 (0.00–0.47) 0.04 (0.00–0.24) 0.15 (0.01–0.35) Non-flooded agriculture 0.59 (0.05–0.90) 0.22 (0.00–0.50) 0.65 (0.29–0.94) Emergent marsh 0.08 (0.00–0.53) 0.17 (0.00–0.84) 0.00 (0.00–0.01) Proximity to high bird-density 2.61 (0.00–26.20) 7.38 (0.00–23.87) 2.42 (0.00–11.78) area (km) Winter Proportion of cover type within a 4.5-km radius Permanent open water 0.03 (0.00–0.24) 0.03 (0.00–0.48) 0.05 (0.01–0.16) Forested wetland 0.06 (0.00–0.38) 0.04 (0.00–0.24) 0.15 (0.01–0.35) Non-flooded agriculture 0.43 (0.05–0.70) 0.21 (0.00–0.47) 0.64 (0.28–0.94) Emergent marsh 0.08 (0.00–0.50) 0.16 (0.00–0.84) 0.00 (0.00–0.01) Proximity to high bird-density 1.25 (0.00–18.26) 14.67 (1.17–31.63) 8.20 (0.00–48.26) area (km) Southeastern Naturalist G45 M.L. Sieges, J.A. Smolinsky, M.J. Baldwin, W.C. Barrow, Jr., L.A. Randall, and J.J. Buler 2014 Vol. 13, No. 1 an explanatory variable as strong if the 90% confidence interval of the regression coefficient did not span zero. Results After including only potential days during active MBHI management seasons and eliminating days with contaminated radar data, we sampled a total of 125 out of 546 days (23%) for KHGX and 97 out of 420 days (23%) for KLCH in the WGCP. For the MAV, we sampled 113 out of 453 days (25%) for KLZK and 86 out of 453 days (19%) for KNQA. We determined soil-wetness index using an average of 2.8 TM images per season per radar during the management year and an average of 6.4 TM images per season per radar during the prior two years, excluding the spring (Table 2). Daily mean radar reflectivity (i.e., relative bird density) varied considerably between the radars throughout the management periods; the KLZK and KLCH radars showed much higher reflectivity overall (Fig. 3). For all radars, reflectivity peaked during winter management, although the timing differed among radars: peaks occurred for KHGX in early winter peak, KLZK and KNQA in mid-winter, and KLCH in late winter. Overall, we found increases in bird density relative to prior years and relative to non-flooded agriculture (NFA) in the management year for nearly all seasons and radars (Table 3). This finding is indicated by values >1 for the mean standardized reflectivity and the ratio of reflectivity relative to prior years. The exceptions were at sites relative to NFA in the management year within the KNQA radar range in fall (0.91) and the KHGX radar range in spring (0.24). The majority of MBHI Table 2. Sample size (number of days) for determining mean reflectivity from NEXRAD data and mean soil-wetness index from Thematic Mapper data by year, season, and radar. Radar Season Remote sensor KLCH KHGX KLZK KNQA Management year (2010–2011) Fall NEXRAD 9 12 5 8 Thematic Mapper 3 2 3 4 Winter NEXRAD 12 27 41 16 Thematic Mapper 1 4 2 3 Spring NEXRAD 7 10 n/a n/a Thematic Mapper 0 0 n/a n/a Prior years (2008–2010) Fall NEXRAD 14 24 16 20 Thematic Mapper 2 2 3 3 Winter NEXRAD 51 41 51 41 Thematic Mapper 8 14 11 8 Spring NEXRAD 4 11 n/a n/a Thematic Mapper 1 1 n/a n/a Southeastern Naturalist M.L. Sieges, J.A. Smolinsky, M.J. Baldwin, W.C. Barrow, Jr., L.A. Randall, and J.J. Buler 2014 Vol. 13, No. 1 G46 Figure 3. Daily mean relative bird density during the management year at MBHI sites for each radar site. Shaded bars distinguish the periods of active management. Southeastern Naturalist G47 M.L. Sieges, J.A. Smolinsky, M.J. Baldwin, W.C. Barrow, Jr., L.A. Randall, and J.J. Buler 2014 Vol. 13, No. 1 Table 3. Summary statistics for measures of soil wetness and relative bird density (i.e., standard reflectivity) during the year of active management and compared to prior years without management. Statistics are divided among Migratory Bird Habitat Initiative sites in the West Gulf Coastal Plain and the Mississippi Alluvial Valley by radar and season. Sample size is number of sample polygons assessed. West Gulf Coastal Plain Mississippi Alluvial Valley KLCH KHGX KLZK KNQA Variable Mean (range) Mean (range) Mean (range) Mean (range) Fall n = 2743 n = 1616 n = 534 n = 171 Soil-wetness index during management year -0.14 (-0.42–0.03) -0.13 (-0.55–0.04) -0.22 (-0.41– -0.04) -0.19 (-0.29–0.01) Change in soil wetness index from prior years -0.02 (-0.29–0.27) -0.01 (-0.33–0.22) -0.09 (-0.21–0.09) -0.08 (-0.24–0.12) Standard reflectivity during management year 2.33 (0.00–14.85) 2.60 (0.00–20.32) 2.66 (0.08–9.03) 0.91 (0.23–2.29) Reflectivity relative to prior years 2.74 (0.02–96.24) 9.44 (0.03–209.83) 7.82 (0.20–75.50) 1.21 (0.38–2.85) Winter n = 2921 n = 1531 n =5 34 n = 148 Soil-wetness index during management year -0.09 (-0.33–0.06) -0.07 (-0.18–0.03) -0.13 (-0.23–0.02) -0.05 (-0.13–0.02) Change in soil wetness index from prior years 0.00 (-0.24–0.19) 0.01 (-0.13–0.16) -0.03 (-0.13–0.13) 0.03 (-0.03–0.10) Standard reflectivity during management year 1703.38 (0.27–29211.01) 5.06 (0.13–112.62) 29.86 (0.00–415.51) 1.93 (0.10–44.90) Reflectivity relative to prior years 10.27 (0.10–272.19) 5.71 (0.12–91.99) 1.64 (0.05–16.71) 2.80 (0.18–29.10) Spring n = 206 n = 1603 Standard reflectivity during management year 2.45 (0.01–20.29) 0.24 (0.00–7.00) n/a n/a Reflectivity relative to prior years 2.21 (0.01–9.61) 1.97 (0.01–35.51) n/a n/a Southeastern Naturalist M.L. Sieges, J.A. Smolinsky, M.J. Baldwin, W.C. Barrow, Jr., L.A. Randall, and J.J. Buler 2014 Vol. 13, No. 1 G48 area exhibited greater bird use relative to NFA within management year and relative to prior years for fall (area-weighted mean across all radars of 65% and 74%, respectively) and winter (area-weighted mean across all radars of 78% and 82%, respectively) but not during spring (area-weighted mean across all radars of 6% and 42%, respectively) (Table 4). Exceptions for a majority increase in bird use relative to NFA in the management year by radar included KNQA during the fall and KLCH and KHGX in the spring. Additionally, a majority of the area (60%) around KHGX during the spring did not increase in bird use relative to prior years. The magnitude and extent of increases varied among seasons and radars such that the greatest increases in the amount and extent of reflectivity relative to prior years occurred during winter in Louisiana (KLCH) and easternmost Arkansas (KNQA) sites and during fall in Texas (KHGX) and western Arkansas (KLZK) sites (Table 3). The greatest use by birds of MBHI-managed sites relative to NFA occurred during winter at all radars. The greatest responses to MBHI management both within and between years, across all radars and seasons, occurred at Louisiana sites during the winter. Here, over 90% of MBHI area had increased bird use relative to previous years and NFA, such that the average bird density was over 10 times the levels recorded for previous years and over 1700 times levels at NFA sites. Because of our sensitivity to the use of private landowner information, we Table 4. Proportion of MBHI area that increased in soil wetness and bird use from prior years and with greater bird use relative to non-flooded agriculture areas during the management year by season and radar. Radar Season KLCH KHGX KLZK KNQA Fall Total hectares assessed 7613 6445 1964 611 Proportion w/ increased mean soil wetness 0.44 0.43 0.06 0.10 from prior years Proportion w/ mean standardized reflectivity 0.63 0.65 0.81 0.31 greater than 1 during management year Proportion w/ increased mean relative reflectivity 0.65 0.82 0.86 0.62 from prior years Winter Total hectares assessed 5884 6102 1964 555 Proportion w/ increased mean soil wetness 0.52 0.54 0.22 0.92 from prior years Proportion w/ mean standardized reflectivity 0.96 0.64 0.73 0.50 greater than 1 during management year Proportion w/ increased relative reflectivity 0.91 0.86 0.46 0.78 from prior years Spring Total hectares assessed 512 6400 n/a n/a Proportion w/ mean standardized reflectivity 0.35 0.04 n/a n/a greater than 1 during management year Proportion w/ increased relative reflectivity 0.63 0.40 n/a n/a from prior years Southeastern Naturalist G49 M.L. Sieges, J.A. Smolinsky, M.J. Baldwin, W.C. Barrow, Jr., L.A. Randall, and J.J. Buler 2014 Vol. 13, No. 1 do not present maps of these results with individual MBHI areas identified. Rather we provide data from an example MBHI area to illustrate the strong bird response during winter at a Louisiana location (Fig. 4). The weakest bird response to MBHI management overall occurred during the spring in Texas. Mean soil-wetness index during the management year nearly always indicated average non-flooded soil conditions at sites during fall and winter (Table 3). However, there were usually areas that were flooded within MBHI site boundaries even if the entire site was not flooded (see Fig. 4). The change in mean fall soil-wetness index from prior years was always negative, indicating drier soil in the management year. However, it was slightly positive for the KHGX and KNQA radars in winter. Soil wetness was greatest during winter, though only slightly more than half of the MBHI area in the WGCP was considered flooded. During winter in the MAV, nearly all of the MBHI area was flooded at KNQA, but less than a quarter was flooded at KLZK. The lower soil wetness during fall is consistent with the fall Figure 4. Images of remotely-sensed soil-wetness and radar-reflectivity data at 8 MBHI sites (outlined) within Louisiana. As depicted by imagery from single dates, MBHI sites are mostly flooded by surface water during the management year (top right panel) and relatively dry during a prior year (top left panel). Mean standardized radar reflectivity at the onset of evening flight (i.e., relative bird density) is greater within and around MBHI sites during the winter of the management year (bottom right panel) than during the previous two winters (bottom left panel). Southeastern Naturalist M.L. Sieges, J.A. Smolinsky, M.J. Baldwin, W.C. Barrow, Jr., L.A. Randall, and J.J. Buler 2014 Vol. 13, No. 1 G50 moist-soil–management techniques for shorebirds, and the higher soil wetness in winter is consistent with the open-water-management methodology used for wintering waterfowl. Bird-response modeling Fall. During fall, the global models generally explained less than half of the variation in relative bird density within the management year (Table 5) and relative to prior years (Table 6). At both radars within the WGCP, the most important variable in explaining bird density within the management year was proximity to areas of high bird density, such that bird density on MBHI sites increased with proximity to high bird-density areas. Additionally, bird density at Texas sites increased with greater soil wetness. Within central Arkansas, however, the amount of forested wetlands in the landscape was most important in explaining bird density within the management year, such that bird density was higher at sites located near forested wetlands. The importance and direction of the relationship of variables explaining the change in bird density relative to prior years differed among all three radars (Table 6). In Texas, MBHI areas with less nearby open water and forested wetland and greater emergent marsh had a greater increase in density relative to prior years. In Louisiana, MBHI areas located near sites with high bird-density areas and with more open water had a greater increase in density relative to prior years. In central Arkansas, MBHI areas farther from high bird-density areas and with lower soil wetness relative to prior years had a greater increase in density relative to prior years. Winter. During winter, the global models generally explained >70% of the variation in relative bird density within the management year (Table 7). At all radars, the most important variable in explaining standardized bird density within the management year was proximity to areas of high bird density, such that bird density was higher on MBHI sites located near high bird-density areas. Additionally, within the WGCP, bird density was positively related to greater amounts of emergent marsh in the surrounding area. In Louisiana, MBHI sites with more non-flooded agriculture and higher soil wetness also had greater bird density. In Arkansas, MBHI areas with more non-flooded agriculture and open water in the landscape had greater standardized bird density in the management year. During winter, the global models did not explain as much variability in bird density relative to prior years as they did for standardized bird density within the management year; however, they still explained >50% of the variation (Table 8). The variation in winter bird density relative to prior years was explained by greater amounts of emergent marsh in the surrounding landscape at both WGCP radars. Otherwise, the importance and direction of the relationship of variables explaining the change in bird density relative to prior years differed among all three radars. In Texas, MBHI sites that were located closer to areas of high bird density and had less open water also had a greater increase in density relative to prior years. In Louisiana, MBHI areas with greater non-flooded agriculture and a larger increase in soil wetness also had a greater increase in density relative to prior years. In central Arkansas, MBHI areas with more open water and forested wetland had a greater increase in density relative to prior years. Southeastern Naturalist G51 M.L. Sieges, J.A. Smolinsky, M.J. Baldwin, W.C. Barrow, Jr., L.A. Randall, and J.J. Buler 2014 Vol. 13, No. 1 Table 5. Mean relative variable importance, mean effect size and effect frequency of explanatory variables in explaining fall standardized bird density within the management year at MBHI areas based on a candidate set of linear regression models (63 models for KLCH and KHGX, 31 models for KLZK). Each model set assessed using a set of 25 samples with 20 sample polygons for each sampling set. Effect size is the mean standardized regression coefficient across all models averaged across sample sets ± unconditional SE. Effect frequency is the proportion of sample sets for which the variable exhibited a strong effect. Characteristic scale (landscape radius in km) at which each land-cover type was quantified in parentheses. The mean global model R2 values were 0.48 (KLCH), 0.54 (KHGX), and 0.40 (KLZK). * indicates the variable of greatest importance and other variables with importance above 0.5 and/or effect frequency above 0.33. KLCH KHGX KLZK Mean Mean Mean Mean effect Effect Mean effect Effect Mean effect Effect Explanatory variable importance size ± SE frequency importance size ± SE frequency importance size ± SE frequency Soil-wetness index 0.38 -0.12 ± 0.11 0.28 0.58* 0.39 ± 0.07* 0.56* 0.39 -0.11±0.06 0.28 Non-flooded agriculture (4.5/4.5/0.5 km) 0.34 -0.20 ± 0.06 0.12 0.33 0.16 ± 0.13 0.12 0.42 0.18 ± 0.08 0.24 Forested wetland (2.5/2.5/4.5 km) 0.33 0.06 ± 0.08 0.16 0.29 -0.03 ± 0.05 0.16 0.58* 0.37 ± 0.10* 0.48* Permanent open water (3.0/4.0/4.5 km) 0.46 0.35 ± 0.03 0.24 0.40 -0.31 ± 0.04 0.24 0.32 -0.06 ± 0.05 0.12 Proximity to high bird-density area 0.47* -0.33 ± 0.14* 0.32* 0.61* -0.44 ± 0.04* 0.60* 0.35 -0.14 ± 0.06 0.16 Emergent marsh (4.5/3.5/n/a km) 0.35 -0.24 ± 0.13 0.08 0.38 0.19 ± 0.16 0.16 n/a n/a n/a Southeastern Naturalist M.L. Sieges, J.A. Smolinsky, M.J. Baldwin, W.C. Barrow, Jr., L.A. Randall, and J.J. Buler 2014 Vol. 13, No. 1 G52 Table 6. Mean relative variable importance, mean effect size and effect frequency of explanatory variables in explaining fall ratio of bird density during the management year relative to the prior two years at MBHI areas based on a candidate set of linear regression models (63 models for KLCH and KHGX, 31 models for KLZK). Each model set assessed using a set of 25 samples with 20 sample polygons for each sampling set. Effect size is the mean standardized regression coefficient across all models averaged across sample sets ± unconditional SE. Effect frequency is the proportion of sample sets for which the variable exhibited a strong effect. Characteristic scale (landscape radius in km) at which each land-cover type was quantified in parentheses. The mean global model R2 values were 0.45 (KLCH), 0.58 (KHGX), and 0.41 (KLZK). * indicates the variable of greatest importance and other variables with importance above 0.5 and/or effect frequency above 0.33. KLCH KHGX KLZK Mean Mean Mean Mean effect Effect Mean effect Effect Mean effect Effect Explanatory variable importance size ± SE frequency importance size ± SE frequency importance size ± SE frequency Change in soil-wetness index 0.31 -0.04 ± 0.06 0.08 0.39 0.09 ± 0.10 0.28 0.46* -0.18 ± 0.09* 0.36* Non-flooded agriculture (1.0/4.5/4.5 km) 0.34 0.12 ± 0.07 0.12 0.43 0.47 ± 0.16 0.32 0.34 -0.10 ± 0.05 0.16 Forested wetland (1.5/4.5/0.5 km) 0.32 0.14 ± 0.06 0.16 0.48* -0.38 ± 0.06* 0.40* 0.30 -0.08 ± 0.04 0.08 Permanent open water (4.0/3.0/2.0 km) 0.50* 0.35 ± 0.07* 0.40* 0.55* -0.43 ± 0.07* 0.48* 0.34 0.08±0.06 0.20 Proximity to high bird-density area 0.52* -0.33 ± .10* 0.44* 0.29 0.10 ± 0.06 0.04 0.59* 0.37 ± 0.03* 0.52* Emergent marsh (4.5/2.0/n/a km) 0.37 -0.03 ± 0.13 0.16 0.49* 0.41 ± 0.16* 0.36* n/a n/a n/a Southeastern Naturalist G53 M.L. Sieges, J.A. Smolinsky, M.J. Baldwin, W.C. Barrow, Jr., L.A. Randall, and J.J. Buler 2014 Vol. 13, No. 1 Table 7. Mean relative variable importance, mean effect size and effect frequency of explanatory variables in explaining winter standardized bird density within the management year at MBHI areas based on a candidate set of linear regression models (63 models for KLCH and KHGX, 31 models for KLZK). Each model set assessed using a set of 25 samples with 20 sample polygons for each sampling set. Effect size is the mean standardized regression coefficient across all models averaged across sample sets ± unconditional SE. Effect frequency is the proportion of sample sets for which the variable exhibited a strong effect. Characteristic scale (landscape radius in km) at which each land-cover type was quantified in parentheses. The mean global model R2 values were 0.88 (KLCH), 0.71 (KHGX), and 0.86 (KLZK). * indicates the variable of greatest importance and other variables with importance above 0.5 and/or effect frequency above 0.33. KLCH KHGX KLZK Mean Mean Mean Mean effect Effect Mean effect Effect Mean effect Effect Explanatory variable importance size ± SE frequency importance size ± SE frequency importance size ± SE frequency Soil-wetness index 0.44* 0.16 ± 0.02* 0.36* 0.36 0.01 ± 0.06 0.24 0.37 0.11 ± 0.01 0.28 Non-flooded agriculture (4.0/3.5/4.5 km) 0.70* 0.33 ± 0.03* 0.72* 0.33 -0.04 ± 0.17 0.16 0.49* 0.27 ± 0.02* 0.36* Forested wetland (4.0/0.5/4.5 km) 0.28 0.06 ± 0.04 0.16 0.25 0.06 ± 0.02 0.04 0.36 0.15 ± 0.02 0.20 Permanent open water (4.5/4.5/4.5 km) 0.31 0.04 ± 0.02 0.20 0.42 -0.26 ± 0.14 0.28 0.70* 0.29 ± 0.01* 0.76* Proximity to high bird-density Area 0.91* -0.63 ± 0.04* 0.92* 0.78* -0.54 ± 0.06* 0.80* 1.00* -0.70 ± 0.01* 1.00* Emergent marsh (1.5/ 4.5/n/a km) 0.69* 0.32 ± 0.03* 0.68* 0.78* 0.65 ± 0.08* 0.80* n/a n/a n/a Southeastern Naturalist M.L. Sieges, J.A. Smolinsky, M.J. Baldwin, W.C. Barrow, Jr., L.A. Randall, and J.J. Buler 2014 Vol. 13, No. 1 G54 Table 8. Mean relative variable importance, mean effect size and effect frequency of explanatory variables in explaining winter ratio of bird density during the management year relative to the prior two years at MBHI areas based on a candidate set of linear regression models (63 models for KLCH and KHGX, 31 models for KLZK). Each model set assessed using a set of 25 samples with 20 sample polygons for each sampling set. Effect size is the mean standardized regression coefficient across all models averaged across sample sets ± unconditional SE. Effect frequency is the proportion of sample sets for which the variable exhibited a strong effect. Characteristic scale (landscape radius in km) at which each land-cover type was quantified in parentheses. The mean global model R2 values were 0.68 (KLCH), 0.57 (KHGX), and 0.51 (KLZK). * indicates the variable of greatest importance and other variables with importance above 0.5 and/or effect frequency above 0.33. KLCH KHGX KLZK Mean Mean Mean Mean effect Effect Mean effect Effect Mean effect Effect Explanatory variable importance size ± SE frequency importance size ± SE frequency importance size ± SE frequency Change in soil-wetness Index 0.57* 0.34 ± 0.03* 0.68* 0.36 0.00 ± 0.09 0.24 0.33 0.15 ± 0.03 0.16 Non-flooded agriculture (4.5/1.5/4.5 km) 0.73* 0.53 ± 0.05* 0.76* 0.39 0.07 ± 0.14 0.24 0.40 -0.20 ± 0.09 0.24 Forested wetland (3.5/3.5/3.5 km) 0.40 0.11 ± 0.11 0.32 0.41 -0.08 ± 0.16 0.24 0.55* 0.34 ± 0.10* 0.44* Permanent open water (4.0/4.5/2.0 km) 0.38 0.05 ± 0.05 0.28 0.47* -0.37 ± 0.12* 0.40* 0.55* 0.34 ± 0.04* 0.48* Proximity to high bird-density area 0.31 -0.01 ± 0.08 0.16 0.44* -0.24 ± 0.11* 0.36* 0.37 0.23 ± 0.02 0.16 Emergent marsh (1.0/3.5/n/a km) 0.56* 0.33 ± 0.03* 0.52* 0.63* 0.57 ± 0.18* 0.56* n/a n/a n/a Southeastern Naturalist G55 M.L. Sieges, J.A. Smolinsky, M.J. Baldwin, W.C. Barrow, Jr., L.A. Randall, and J.J. Buler 2014 Vol. 13, No. 1 Discussion We used weather-surveillance radar to quantify relative bird densities at the onset of evening flights to determine the efficacy of the MBHI in providing diurnal habitat for waterbirds across a broad spatial and temporal scale. Our analysis indicated that on the majority of managed MBHI lands, bird densities increased when compared to prior non-managed years and were often higher than densities found on surrounding non-flooded agricultural land. There were marked differences in relative magnitude of bird responses across seasons and regions, with the greatest bird responses to MBHI management observed within the WGCP region during winter. For example, winter bird use on over 90% of radar-observed MBHI area within Louisiana increased by an average of over 10 times relative to previous years. The density of birds was lower and their relative responses were weaker during the fall, likely due to the short duration and late timing of fall management with respect to shorebird migration. The weakest bird response to MBHI activities was during spring in the WGCP, for which we could not remotely assess moist soil management. We expected to see temporal and geographic differences because bird numbers and species composition changed during different management periods and in space due to differences in local and regional characteristics of the landscape. Various groups of birds migrate through the area throughout the year; land birds and shorebirds pass through first in spring and fall followed by waterfowl that often stay through the winter (Tamisier 1976). We analyzed fall management activities that occurred during October, when the majority of shorebirds had already passed through and only a few species, such as Limnodromus spp. (dowitchers), Calidris minutilla (Least Sandpiper) and Tringa spp. (yellowlegs), were still migrating (Ranalli and Ritchison 2012, Robbins and Easterla 1991, Twedt et al. 1998). Land-bird migration, however, is near its peak along the Gulf Coast in October (Gauthreaux and Belser 1999). Flights of early migrant waterfowl such as Northern Pintail and Anas discors L. (Blue-winged Teal) begin as early as September (Cox and Afton 1996, 1997; eBird 2013; Tamisier 1976), but the first big influx of wintering waterfowl generally occurs in early November (Tamisier 1976). Based on surveys conducted around sunset (i.e., close to when NEXRAD sampled the airspace over MBHI sites), waterfowl and wading birds were generally more abundant in the airspace than land birds and shorebirds over MBHI fields in Louisiana during the month of October 2011 (W.C. Barrow, unpubl. data). Thus, radars would have observed a mix of land birds, shorebirds, and early waterfowl engaging in evening migratory flights during October. The presence of diverse species during evening flights, especially migrating land birds, may account for in part why our models explained less variability in fall bird densities for both regions compared to variability in winter densities. During fall management in the MAV, when migrating land birds were dominant, bird densities at MBHI sites were positively associated with forested wetlands. Areas with more forested wetlands in the surrounding area had higher bird densities during the management year, likely indicating of the presence of migrating land birds from adjacent forested habitats where land-bird migrants are known to Southeastern Naturalist M.L. Sieges, J.A. Smolinsky, M.J. Baldwin, W.C. Barrow, Jr., L.A. Randall, and J.J. Buler 2014 Vol. 13, No. 1 G56 concentrate (Buler and Moore 2011, Gauthreaux and Belser 1999). Additionally, some waterfowl species such as Anas crecca L. (Green-winged Teal) and Northern Pintail use forested wetlands in the MAV throughout the spring and fall (Heitmeyer 1985). Our data also indicate that many sites in the MAV were not actually flooded in October and that drier sites were weakly associated with a greater increase in bird density in the management year relative to prior years. During fall management in the MAV, sites were drier than those in the Gulf, and observed bird densities may reflect shorebirds using drier mudflat sites, land birds (e.g. blackbirds) en route to their roosts, or neotropical migrant land birds departing the nearby forested wetlands and utilizing the landscape adjacent to the sites. During fall and winter, the only variable that exhibited a consistent relationship with bird density between the two radars within the WGCP was proximity to a high bird-density area. Established areas of high waterbird densities and the tendency of waterbirds to form traditional large roosting flocks (Tamisier 1985) are two likely reasons why we saw greater increases at sites close to high bird-density areas. Large concentrations of waterfowl have historically used the marshes and adjacent wetprairie lands situated along the Gulf Coast (Bateman et al. 1988, Bellrose 1976, Tamisier 1976). An estimated 4 million ducks and hundreds of thousands of geese wintered in coastal Louisiana in the late 1960s (Lynch 1975, Tamisier 1976), with a more recently estimated 4 million waterfowl in coastal Texas (US Fish and Wildlife Service 1999). The MAV has also historically harbored millions of waterfowl, with the number of wintering Anas platyrhynchos L. (Mallards) estimated at 1.5 million (Bellrose 1976). A great portion of the extensive coastal prairie and its associated wetlands along the Gulf Coast support waterbirds, including portions of their historic winter ranges (Eadie et al. 2008), has been converted for rice and other agricultural products, altering the landscape and bird distributions (Hobaugh et al. 1989). Similarly, much of the forested wetland area of the MAV was converted for agricultural use throughout the last century (Forsythe 1985). Despite these changes, the WGCP and the MAV remain two of the most important regions for migrating and wintering waterbirds in North America (Bellrose 1976) as evidenced by the millions of birds that feed and roost in their agricultural fields each year (Hobaugh et al. 1989, Remsen et al. 1991). Communal roosting is characteristic of many shorebird and waterfowl species (Colwell 2010, Tamisier 1976). Some birds use the same winter roost or feeding sites year after year (Tamisier 1985). For example, Cox and Afton (1996) reported high fidelity (71%) of radio-marked female Northern Pintails to Lacassine National Wildlife Refuge in coastal Louisiana following nightly foraging trips to nearby agricultural land. Additionally, although changes in flooding occurred on the landscape throughout the winter, ducks maintained consistent flight directions when leaving Lacassine National Wildlife Refuge (Tamisier 1976). Within Louisiana, radar observations indicate birds are concentrated in marsh and agricultural areas within and around Lacassine and Cameron Prairie National Wildlife Refuges and the White Lake Wetlands Conservation Area, all of which are well-known roosting areas for wintering waterfowl (Link et al. 2011). These findings support the idea Southeastern Naturalist G57 M.L. Sieges, J.A. Smolinsky, M.J. Baldwin, W.C. Barrow, Jr., L.A. Randall, and J.J. Buler 2014 Vol. 13, No. 1 that birds consistently use certain areas during the winter and that these sites may be important predictors of waterbird activity. Regional habitat differences associated with emergent marsh also influenced differential bird responses across the sites. Our finding that increased bird densities at sites in the WGCP region were related to larger amounts of emergent marsh in the surrounding landscape highlights the importance of emergent marsh in predictions of winter bird densities. Generally, waterfowl use of natural wetlands is positively related to the extent of wetlands in the local landscape (Brown and Dinsmore 1986, Fairbairn and Dinsmore 2001, McKinstry and Anderson 2002, Stafford et al. 2007, Webb et al. 2010). Wetland habitats have traditionally supported many waterbirds and are important wintering grounds for ducks and other waterfowl (Tasimier 1976). For example, Link (2011) found that Mallards could acquire most of their energetic requirements from or in close proximity (3–15 km) to marsh habitats even though they engaged in routine flights between diurnal roost sites in marsh and nocturnal foraging sites in agricultural fields. Emergent marshes are often part of large and diverse wetland complexes (Cowardin et al. 1979) that support a diversity of birds (Brown and Dinsmore 1986). Wetland complexes in various stages of succession have proven to be the most beneficial to waterbirds (Fredrickson and Reid 1986, Kaminski et al. 2006, Murkin and Caldwell 2000, Van der Valk 2000, Webb et al. 2010). During winter in the MAV, reflectivity was relativelygreater at sites with more forested wetlands and permanent open water in the landscape relative to the baseline years. In the winter of the management year, the Arkansas Game and Fish Commission (AGFC) noted that waterfowl may have shifted to using more forested wetlands when abnormally cold temperatures produced ice on much of the open water associated with agricultural fields (AGFC 2010a, b). Additionally, dry conditions across Arkansas that winter contributed to a lack of temporary water on the landscape, likely explaining the positive relationship between permanent open water and bird density. Within Arkansas, waterfowl numbers peaked during December 2010 based on aerial surveys (AAGFC 2011a), consistent with the peak in radar reflectivity in the MAV during that time. However, waterfowl numbers were later reduced by 50% during January compared to the previous year likely due to dry conditions across Arkansas (AGFC 2011b). Subsequently, waterfowl numbers in Louisiana increased by 21% relative to the previous year in January 2011 (Louisiana Department of Wildlife and Fisheries 2011). Accordingly, radar reflectivities in Louisiana peaked during January 2011. Although we detected some increases in bird density during spring management in the WGCP region, the increases were slight. Lack of wetness data and few enrolled sites prevented us from investigating how site and landscape variables influenced bird densities. Some water birds may have already departed on migration during the month of March (see Hobaugh et al. 1989). For example, Mallards and Northern Pintails begin leaving wintering grounds in early February (Bellrose 1976), and the majority of ducks depart coastal Texas during the month of February, with few left by mid-March (Hobaugh et al. 1989). A few shorebird species, such Southeastern Naturalist M.L. Sieges, J.A. Smolinsky, M.J. Baldwin, W.C. Barrow, Jr., L.A. Randall, and J.J. Buler 2014 Vol. 13, No. 1 G58 as Recurvirostra americana Gmelin (American Avocet), may leave Texas in early March, (Oberholser 1974), but many shorebirds are present south of the WGCP during March and into April (Withers and Chapman 1993). Alternatively, food resources on local flooded fields may be too depleted by spring to support large groups of waterbirds (Cox and Afton 1996, Hamilton and Watt 1970, Hobaugh et al. 1989). Increases in bird density occurred despite our finding that there was little or no increase in soil wetness at the managed sites. The remotely sensed data that we used to calculate the soil-wetness index may have limited our ability to detect such changes. We had few usable images for each radar station per season with which to calculate the index. Additionally, we had no information about the extent of flooding within individual properties. Thus, a landowner’s contract may have required flooding on only a portion of their property, and our analysis may have included the whole property boundary. Moreover, drought conditions, restricted water supplies, or other circumstances may have prevented landowners from complying fully with their contracts. Soil-wetness values in the MAV region were undoubtedly influenced by natural fluctuations in precipitation patterns. The baseline years comprised a relatively wet period in the MAV; October 2009 in Arkansas was the wettest October recorded in more than 100 years (NOAA National Climatic Data Center 2009). In contrast, much of Arkansas was under drought conditions in 2010 (NOAA National Climatic Data Center 2010). Thus, these conditions complicated quantification of changes in site wetness (i.e., flooding) during the management year. Variability in the intensity of moist soil management can have an important effect on wintering waterfowl use (Kaminski et al. 2006, O’Neal et al. 2008). MBHI sites in the MAV and those in Texas received minimal modifications. In the MAV, contracts simply required landowners to keep surface water on their fields for a specified amount of time across a wide range of depths (5–46 cm) to potentially benefit a variety of shorebirds and wading birds. Surface-water depths are difficult to measure remotely. Regular water-depth measurements in the field would have allowed us to better quantify habitat for particular taxa of wa terbirds. Ranalli and Ritchison (2012) note that mudflat habitat associated with agricultural fields is unpredictable in the MAV because it is precipitation-dependent and varies annually. Thus, management activities associated with the MBHI may have provided stopover habitat for migrating shorebirds. Landowners may have been unable to maintain winter flooding at depths that would benefit waterfowl, but any water on the fields likely benefited shorebirds because they are known to identify and use saturated soils within days of being inundated (Skagen and Knopf 1993, Skagen et al. 2008). The attractiveness of MBHI wetlands to waterfowl may have varied based on the land use of sites prior to flooding. Some fields were pastures (15% in the MAV, 20% in the WCGP; USDA NASS CDL 2010) during the management year and may not have provided much forage in the form of wetland plant seeds during the first year of the program. Rice seeds persist longer in wetlands than other seeds associated with Southeastern Naturalist G59 M.L. Sieges, J.A. Smolinsky, M.J. Baldwin, W.C. Barrow, Jr., L.A. Randall, and J.J. Buler 2014 Vol. 13, No. 1 crop-harvest waste, thereby potentially increasing available forage for waterbirds compared to other flooded crops (Nelms and Twedt 1996). However, only 20% of MBHI sites in the MAV were rice fields, compared to 40% in the WGCP (USDA NASS CDL 2010), which may account for greater positive changes in reflectivity values in the WCGP. Although waterfowl feed on non-flooded waste grain (Bellrose 1976, Kross et al. 2008, Reinecke et al. 1989), flooding rice fields increases habitat for waterfowl and other waterbirds in California (Elphick and Oring 1998). Buler et al. (2012b) found that waterfowl use of restored wetlands was negatively related to the amount of wetlands in the local landscape and speculated that this may be because newly restored wetlands were lower-quality habitat than natural wetlands. Similarly, studies have found that flooded agricultural fields do not necessarily act as surrogates for natural wetlands (Bartzen et al. 2010, Czech and Parsons 2002). Ma et al. (2004) found that although natural wetlands provided better habitat, artificial wetlands attracted some waterbird species during winter. Because portions of the MAV and WGCP have been farmed for rice each year in the last 150 years or so (Hobaugh et al. 1989), waterbirds may be dependent on flooded agricultural fields for wintering habitat, in which case the MBHI provided valuable habitat that landowners otherwise might not have flooded in a dr ought year. In the wake of a major environmental disaster, the MBHI program provided water birds with temporary wetland habitats by flooding agricultural fields in the MAV and WGCP regions. We detected increases in bird densities on the majority of MBHI sites during migration and wintering periods for waterfowl and shorebirds. The greatest relative responses by birds to MBHI sites occurred in the WGCP during the winter management period at sites closer to areas of high previously documented bird density and with more emergent marsh in the surrounding landscape. We are currently conducting a more detailed analysis of bird use at Louisiana MBHI sites in the year subsequent to this study with the addition of ground-survey data, thermal infrared camera recordings, and portable radar observations. These data will provide more insight into bird-use patterns of MBHI sites. For example, our portable radar detected birds using MBHI sites during the night. Bird use of managed lands may be maximized if future enrollments are clustered into a mosaic of wetlands that more closely resemble natural wetland complexes (Brown and Dinsmore 1986). Because predicted climactic changes (Intergovernmental Panel on Climate Change 2007) will likely cause alterations in sites used by wetland-dependent birds, providing habitat for migratory birds in the MAV and WGCP will continue to be important for all stakeholders, particularly with the knowledge that migration is a limiting factor for shorebird and waterfowl success (Afton et al. 1991, Alisauskas and Ankney 1992, Baker et al. 2004, Blums et al. 2005, Morrison et al. 2007, Ryder 1970). Acknowledgments We thank the Natural Resources Conservation Service for funding. The following personnel from the NRCS provided geospatial data, management activity, and/or other related information on MBHI fields: J. Pitre, R. Cheveallier, F. Chapman, S. Romero, B. Lyons, J. Haller, C. Stemmons, J. Baker, G. Clardy, R. 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