DOI: 10.5061/dryad.v9s4mw757
title: Data and code from: Factors influencing shorebird use of post-harvest flooded rice fields in California’s Sacramento Valley

description: Because of the importance of the Central Valley of California, USA, to
 shorebirds along the Pacific Flyway, conservation investments have been
 made in the area’s agricultural fields and managed wetlands. Increasingly,
 landowner incentive programs are being used to deliver shorebird habitat,
 presenting opportunities to answer remaining questions about which
 implementation strategies and field management practices are most
 effective at attracting birds. To provide management guidance for these
 investments, we collected and analyzed 5 years of data (2014-2018) on
 shorebird abundance in flooded rice fields enrolled in a dynamic habitat
 incentive program. The program incentivized flooding in fallow and
 post-harvest rice fields in seasons when habitat is relatively sparse,
 specifically the early fall and late spring. Across nearly 9,000 field
 observations over 5 years, we explored the relationship between abundance
 and density (birds/ha) of shorebirds and vegetation cover, soil clay,
 landscape-level flooding, and local flood timing, duration, and depth. We
 observed more shorebirds in fields that were approximately 50% flooded,
 had water depths of 5-10 cm, and had minimal rice straw or stubble cover,
 with strong or very strong evidence for each of these relationships. We
 found that the timing of habitat provisioning was important, with moderate
 evidence that earlier fall flooding and strong evidence that the duration
 of fall flooding was associated with higher shorebird density. We observed
 lower shorebird densities in locations with ample flooded rice habitat in
 surrounding areas, potentially because shorebirds spread out across the
 landscape. We found very strong evidence that flooding consistency, either
 at a site that was continually flooded over many months or a site that had
 been flooded in previous years, was associated with higher shorebird
 density. Soil clay content was associated with decreased observed
 shorebird density, potentially through its influence on the ability of
 shorebirds to forage for invertebrate prey. These results suggest best
 practices for shorebird habitat creation in agricultural landscapes,
 providing important information for conservation and population recovery.
description: <strong>Bird surveys</strong> We
 monitored enrollment fields distributed across the Sacramento Valley
 (Figure 1) following the sampling design and survey protocol described in
 Golet et al. (2018). Briefly, we selected a set of spatially balanced
 random survey point locations along the edges of fields using the
 generalized random tessellation stratified (GRTS) sampling methodology
 (Stevens and Olsen 2004). The rice fields were composed of multiple checks
 (or patties), and we established only 1 survey point in any given check.
 From these points, we conducted surveys of semi-circular areas within the
 fields that had a fixed radius of 200 m, or less when they intersected
 earthen levees or field boundaries (Reiter et al. 2011, Strum et al.
 2013). While the number of survey areas within an enrollment field
 depended upon its size, we tried to ensure a sampling density of 1 survey
 point per 12 ha. Trained biologists conducted surveys
 and established a common understanding of all data entry queries during
 group training sessions led by a senior biologist in the field at the
 beginning of each year. In addition, surveyors participated in online
 training (from eBird, https://ebird.org, and Sibley Guides,
 https://www.sibleyguides.com) to hone flock counting skills. Many of the
 biologists that collected the data worked on the project for multiple
 years, which brought consistency to data collection. For at least 2
 minutes, and as long as necessary to count all birds present, surveyors
 scanned the survey areas with binoculars and spotting scopes to record all
 visually detectable waterbirds. Surveyors endeavored to count all birds in
 the water and on the field edges, including those that flew off or landed
 during counting. Except for aerial foragers, we did not record birds that
 flew by. Monitoring started when enrollment started. We monitored
 enrollment fields every 5 to 7 days during the enrollment period, and
 continued until the fields were no longer shallowly flooded. A total of
 451 field-seasons were enrolled in the BirdReturns program during the
 study period, where a field-season refers to a particular field enrolled
 in a given season and some fields were enrolled in multiple seasons. We
 surveyed fields an average of 5 to 10 times per year (Table 1).
 Whenever possible, we identified shorebirds to species.
 Exceptions in species-level identifications include dowitchers and
 yellowlegs; we did not distinguish between short- and long-billed
 dowitchers and greater (<em>Tringa melanoleuca</em>) and
 lesser (<em>T. flavipeswere</em>) yellowlegs. We recorded 24
 unique taxa (Figure 2). Following the work of many others studying
 waterbirds in rice (e.g., Elphick 2008, Sesser et al. 2018), we did not
 calculate detection probabilities. We assume detection probabilities were
 high owing to minimal vegetation and open sightlines; although, some
 species were likely undercounted because of their secretive behavior
 (e.g., Wilson’s snipe [<em>Gallinago delicata</em>]) or small
 size (e.g., least sandpiper [<em>Calidris
 minutilla</em>]). <strong>Site-level and
 landscape characteristics and covariates</strong>
 At each site, we measured 2 types of characteristics: site-level
 (local) and extended temporal and landscape variables (landscape; Table
 2). The surveyor visually estimated local characteristics during the same
 time windows that they conducted bird counts. Local characteristics
 included the fraction of the survey area that was either flooded, moist,
 or dry (summing to ≤100%) and the fraction of the survey area that was
 bare earth, non-rice vegetation, standing dead rice stubble, or horizontal
 (cut down) rice straw (which could sum to greater than 100% because of
 overlapping categories). Following methods established in previous studies
 that characterized water depth in rice fields (Sesser et al. 2018), we
 determined depth (to the nearest 2.5 cm) by viewing 2 stakes in the survey
 area that were painted with 5-cm-wide color bands. The first stake was
 positioned 50 m from the observer point location and the second was 200 m
 from the observer location, at the distant boundary of the survey area. We
 used the mean of these 2 depth measurements to represent the depth in the
 enrollment field (as described in Golet et al. 2018). We placed the stakes
 in the survey area after exploring the region for locations that were
 representative of a particular field’s elevation. A pair of properly
 placed stakes are sufficient to characterize depth in Sacramento Valley
 rice where about 66% of the fields are set up to grow only rice and have
 little to no slope. Fields that rotate between row and field crops
 typically have slopes of only 0.05% to 0.1% (University of California
 2023). Within each survey area, we measured the average
 percent clay. Using a 100-m resolution, gridded map of the fraction of
 clay in soil (Ramcharan et al. 2018; derived from SSURGO data), we
 averaged the amount of soil within a survey area after averaging percent
 clay across soil depth layers. We calculated landscape
 spatial variables from satellite images taken by the Landsat 8 Operational
 Land Imager and Thermal Infrared Sensor using a classification model to
 estimate the amount of flooded habitat that was available in the
 Sacramento Valley (Reiter et al. 2015, 2018<em>a</em>,
 2018<em>b</em>). Because cloud cover can mask imagery of the
 Earth’s surface, we generated estimates of flooding for days with
 cloud-filled images using models relying on the average probability of
 water in that pixel in that month based on the previous 10 years, the
 normalized difference water index value from Moderate Resolution Imaging
 Spectroradiometer (MODIS), and the proportion of open water in the rest of
 the Central Valley Joint Venture planning basin (Reiter et al.
 2018<em>a</em>). Within the Landsat time
 window in which we surveyed birds, we considered the amount of flooded
 rice and wetlands in the larger landscape within a 2-km, 5-km, and 10-km
 radius around the survey area. These covariates allowed us to consider
 whether the amount of flooded rice or wetland influenced shorebird use of
 the survey areas. To explore whether fields or landscapes that were
 predictably flooded across years would attract more birds, we also
 considered flooded rice and wetlands in the larger landscape in the
 preceding year and the preceding 3 years. Given that the amount of
 flooding within the broader landscape in a given year is highly correlated
 (<em>r </em>&gt; 0.8) to landscape flooding in the
 preceding year and preceding 3 years, we considered only landscape-level
 variables from the year in which we made observations. The proportion of
 the landscape that was flooded within the survey area in the months or
 years preceding enrollment was not highly correlated across years and thus
 could be included in the models together with contemporaneous flooding.
 For spring enrollment only, we considered flooding in October through
 January leading up to the spring enrollment. To calculate this variable,
 we averaged the flooded area within the survey area across available
 16-day images from October to January leading up to the bird
 observation. In addition to landscape characteristics,
 we tested whether the total number of shorebirds in a survey area was
 influenced by the start date of enrollment, duration of enrollment, or
 size of enrollment fields. Across a season, all survey areas within an
 enrollment field shared the same values for these 3 variables. Finally, we
 included the month of the bird observation to account for seasonality in
 shorebird use of enrollment fields. In the fall, there were few
 observations prior to September so we pooled observations from August and
 September (as if they were the same month: September). We also included an
 interaction between observation month and landscape-level rice and wetland
 flooding because we expected that seasonality would affect how shorebirds
 use the larger landscape. None of the variables in the
 spring or fall had pair-wise correlation coefficients greater than
 <em>r = </em>0.6. In fall and spring, respectively, only 8 and
 5 pairs of coefficients had positive or negative correlations greater than
 <em>r = </em>0.27 (Figure S1, available in Supporting
 Information). In models that did not include quadratics or interaction
 terms, variance inflation factors for all covariates in spring and fall
 models were &lt;1.75. <strong>Data
 management</strong> Data were curated by The
 Nature Conservancy staff until transfer to E. Conlisk for derivation of
 remotely sensed landscape cahracteristics and analysis.
description: # Data and Code for "Factors influencing shorebird use of
 post-harvest flooded rice fields in California’s Sacramento Valley"
 [https://doi.org/10.5061/dryad.v9s4mw757](https://doi.org/10.5061/dryad.v9s4mw757) ## Description of the data and file structure The zipped folder contains three files that support the results in the paper "**Factors influencing shorebird use of post-harvest flooded rice fields in California’s Sacramento Valley**" by Conlisk et al. 2024. ### Files and variables #### File: Repository.zip **Description:**  The zipped folder contains three files: Data08082024.csv – the data used for all analyses in the paper “Factors influencing shorebird use of post-harvest flooded rice fields in California’s Sacramento Valley” by Conlisk et al. 2024 Metadata.xlsx – descriptions of all the columns within the data file Data08082024.csv AnalysisCode.R – the code used to analyze data and calculate summary statistics All results in the paper should be repeatable with these files.  The locations of survey plots are not provided to protect the privacy of the farmers enrolled in the BirdReturns program.  ## Code/software Please see the code in the file "AnalysisCode.R". ## Access information Other publicly accessible locations of the data: * The remotely sensed data derived in this study can be found at the Point Blue Water Tracker website: https://data.pointblue.org/apps/autowater/ Data was derived from the following sources: * Data was obtained from bird and site observations by The Nature Conservancy staff.