Study sites
Knots present in Florida in March mainly winter in the Southeast United States and Caribbean but not necessarily in Florida [10] and knots stopping in South Carolina in April and May come from all four wintering populations [8,13,23]. We thus focused our work primarily on red knots using the South Carolina coast in April and May, and to a lesser extent the Gulf Coast of Florida. In April 2017 and 2018 we captured red knots roosting in flocks of up to 3,000 individuals on Seabrook Island, (32O34’N, 80O9’W) in Charleston County, South Carolina. Seabrook Island and adjacent Kiawah Island, during the last decade, have supported spring flocks of over 8,000 red knots [24]. Both Kiawah and Seabrook Islands are recreational destinations with substantial beach traffic in April and May. During May 2019 we captured red knots feeding on horseshoe crab (Limulus polyphemus) eggs at Deveaux Bank Seabird Sanctuary (32O32’N, 80O11’W), a nearshore island owned by South Carolina Department of Natural Resources and primarily managed for waterbird conservation. We captured a single red knot in May 2019 at Turtle Island Wildlife Management Area in Jasper County, SC (32O4′ N, 80O 53′ W). Florida captures were restricted to a single season in March 2019 at Fort de Soto Park in Pinellas County (27O37’N, 82O43’).
Bird capture and tag deployment
Red Knots were captured in predominantly single species flocks with cannon nets, removed immediately, and placed in holding cages for processing. Birds were aged using Pyle [25]. We recorded morphometric measurements, weighed, and fitted each knot with a USGS incoloy band and a uniquely inscribed 3-character flag. To prepare a bird for transmitter attachment, we clipped a small patch of feathers above the uropygial gland, and affixed the digitally coded VHF transmitters (166.38 MHz with 4.7 to 11.3 second burst intervals; NTQB2-4-2, Lotek Wireless; hereafter “nanotag”) with a polyacrylamide glue [26]. Nanotags weighed approximately 1 g (< 1% of red knot body mass) and measured 12 x 8 x 8 mm with an 18 cm long external wire antenna.
Processing detection data
We processed data following methods of prior studies using Motus [21,27,28]. Tag identity is encoded in the duration of three rapid, consecutive pulses comprising a single tag ‘burst’ in combination with the precisely fixed interval between these bursts; the pattern of pulse lengths and burst interval is unique among tags. We eliminated false detections during post-processing by examining all detections with less than three consecutive pulses and considering several derived metrics of detection structure related to a tag’s frequency, burst interval, and other signal qualities, as well as considering the noise context of the receiving station and other valid detections of the tag.
Departure dates
We examined the detection history of each individual red knot to calculate the day of departure from the South Carolina coast (but not for the smaller sample of Florida birds). To estimate the departure window from coastal South Carolina to northern destinations we used two methods. First, for birds that were detected by a station away from the coast within South Carolina or North Carolina (n = 10), we considered the date of first detection away from the capture site as the initiation of migration. At the time of this study only one (2017), two (2018) and six (2019) receiving stations were operational in South Carolina, and none within 75km of the capture location. The lack of stations near the capture location made it difficult to estimate departure date precisely for most individuals. To make use of all birds detected during northbound migration, we thus assigned the 37 knots with uncertain departure dates a range of potential departure dates. We assigned each day between the tag deployment date and the date of first detection away from the capture site a percentage that reflected the possibility the bird departed on that day. For example, a knot with a known departure date made its full contribution (1) to a date, whereas a knot with a 5-day potential departure window contributed .2 to each of the days in that date range. Summing these departure date weights for all 47 individuals produced an estimated departure timeline for our full data set while accounting for uncertainty in exact departure dates.
Migration pathways
Similar to Sanders et al. (in press), we compared the relative use of the Atlantic Coastal route through Delaware Bay versus an inland route using patterns of tag detections in two primary watersheds: Delaware Bay, which we defined as any stations within 30 km of the (HUC 02040204) Delaware Bay Hydrological Unit [29], and the Great Lakes Basin as the 5 lakes and their associated subbasin watersheds [30]. Migratory routes were typically quite distinct in the detection dataset and were scored as follows. We classified individuals with multiple detections within Delaware Bay separated by more than one day to have “stopped” in Delaware Bay. We classified a single individual to have “likely stopped” in Delaware Bay as it was detected multiple times over two or more days but just outside our defined watershed. An individual was deemed to have “skipped” Delaware Bay when the detection history indicated a direct, or nearly so, flight between the coast of South Carolina and a station north of Delaware Bay without time for a stopover in Delaware Bay. An individual was scored to have “likely skipped” Delaware Bay if they were not detected within, or only during a direct flight through, the Delaware Bay watershed and the detection history suggested time was too constrained for a stop in Delaware Bay, or the trajectory of the detection path seemed inconsistent with use of the mid-Atlantic coast. We classified use of Delaware Bay as “unknown” when a red knot was never detected in Delaware Bay but the spatiotemporal patterns of detection lacked sufficient resolution to distinguish between strategies. We explored a possible relationship between individual body condition and migratory strategy with a linear model of body mass at capture and assigned migration strategy.
Stopovers
We defined stopover locations, based on detections more than 50 km from the tagging location, as either detections at a single station spanning > 4 hours with no intervening detections elsewhere, or spanning > 6 hours among multiple stations within 30 km of each other [27]. Although Crysler et al. [27] identified stopovers as detections at a single station spanning > 8 hours (with no intervening detections elsewhere) or spanning > 10 hours among multiple adjacent stations, we relaxed those timeframes because shorebirds migrating during the day may elect to temporarily rest or refuel (i.e., mix the acts of migration and stopover [31] for shorter time periods. Bayly et al. [32] proposed distinguishing between “true” stopover during multiday refueling stops and brief stops to rest stops of < 24 hours, but that finer-scale distinction is beyond the scope of this dataset. To visualize areas that were used repeatedly for stopovers, we buffered the stations associated with stopover detections for each individual by 50 km and overlaid the resulting polygons to generate a rough stopover “heat map.”
Speed of travel and wind assistance
We estimated migration speeds for knots during migratory flights using detection times at receiving stations separated by at least 150 km (to exclude local movements and reduce bias from uncertainty in an individual’s precise location during a detection; [28], and restricted estimates of migration speed to flights of 18 hours or less. This resulted in 40 flight trajectories representing 30 individuals for analysis of travel speed and wind assistance.
We calculated total trajectory length as the shortest distance connecting all receiving stations passed during the flight between the beginning and ending receiving stations, and trajectory displacement length as the shortest distance between the beginning and ending receiving station, using the `trajr` package [33] in R (version 4.1.2; [34]. We calculated net ground speed for a flight as the trajectory displacement length divided by the time between the last detection at the beginning receiving station and the first detection at the ending receiving station. We permitted an individual to contribute multiple flights, provided there was no receiver station overlap in the trajectories. For each migratory flight, we also estimated tailwind support at departure using surface wind conditions (i.e., 1000 hPa pressure level) at the measurement time closest to departure. We assumed the “preferred” direction of movement (as required by the calculation of tailwind support) was the bearing that would take the bird to the ending receiving station following a great-circle route. We acquired wind component data for each migratory flight from the NCEP/NCAR Reanalysis project [35] via the RNCEP package [36] in R (version 4.1.2; [34].