Pulsed resources resulting from animal migrations represent important, transient influxes of high resource availability into recipient communities. The ability of predators to respond and exploit these large increases in background resource availability, however, may be constrained when the timing and magnitude of the resource pulse vary across years. In coastal Newfoundland, Canada, we studied aggregative responses of multiple seabird predators to the annual inshore pulse of a key forage fish species, capelin (Mallotus villosus). Seabird aggregative responses to fish biomass were quantified from weekly hydroacoustic and seabird surveys during July-August within an annually persistent foraging area (10 km²) associated with a cluster of capelin spawning sites across 10 years (2009-2010, 2012, 2014-2020). Seabird predators included breeding members of the families Alcidae (Common Murres Uria aalge, Razorbills Alca torda, Atlantic Puffins Fratercula arctica) and Laridae (Great Black-backed Gulls Larus marinus, American Herring Gulls L. argentatus smithsonianus) and Northern Gannets Morus bassanus, along with non-breeding, moulting members of the Family Procellariidae (Sooty Shearwaters Ardenna griseus, Great Shearwaters A. gravis). The inshore migration of spawning capelin resulted in 5-619 times (mean ± SE, 146 ± 59 times) increase in coastal fish biomass along with a shift toward more, larger and denser fish shoals. Within years, seabird abundance did not increase with inshore fish biomass but rather peaked near the first day of spawning, suggesting that seabirds primarily respond to the seasonal resource influx rather than short-term variation in fish biomass. Across years, the magnitude of the seabird aggregative response was lower during low-magnitude resource pulse years, suggesting that predators are unable to perceive low-magnitude pulses, avoid foraging under high competitor densities, and/or shift dietary reliance away from capelin under these conditions. The seabird response magnitude, however, was higher when the resource pulse was delayed relative to the long-term average, suggesting that predators increase exploitation during years of minimal overlap between the resource pulse and energetically demanding periods (e.g., breeding, moulting). This long-term study quantifying responses of multiple predators to a pulsed resource illustrates the ability of natural systems to tolerate natural and human-induced disturbance events.

A fine-scale (16-20 km) survey was conducted within the biological hotspot on the northeast Newfoundland coast approximately weekly throughout July-August over 10 years (2009-2010, 2012, 2014-2020). During surveys, a standard strip transect method was used to record seabird counts (Method I, Tasker et al. 1984), whereby continuous counts of all seabird species were made by one or two observers from the bridge (~2 m above sea level) in a 90° arc from the bow out to 300 m on the port side of the vessel. High-resolution hydroacoustic data were recorded simultaneously using a Biosonics DTX 6000 scientific hydroacoustic system (BioSonics Inc., Seattle, Washington). The sounder was operated through a 70 kHz split-beam transducer (15° beam angle) calibrated with a tungsten carbide sphere and mounted in a towed body. The transducer was towed on the starboard side of the vessel ~1 m below the surface and, thus, acoustic signals were not reliable until ~3 m. The sounder was operated at 1 ping s^-1 and pulse duration of 0.4 ms. Raw high resolution hydroacoustic data (s_v, volume backscattering coefficients) were continuously acquired above a threshold of -90 dB.

Echoview software (version 4, SonarData, Myriax Software Pty. Ltd., Hobart, Tasmania) was used to analyze the hydroacoustic data. Acoustic signals within 0.5 m of the seabed were first omitted if the seafloor could not be distinguished from biology (e.g. side-lobing; Simmonds and MacLennan 2005). To quantify acoustic biomass (area backscattering coefficient, or s_a, m²·m^-2; MacLennan et al. 2002), we integrated acoustic signals using a minimum s_v threshold of -80 dB in 100 m segments along the survey. This threshold allowed the detection of single capelin targets in this shallow area (15-50 m), while filtering out most other noise. Acoustic signals are primarily due to capelin nearby the spawning sites (Davoren et al. 2006), although sand lance (Ammodytes spp) is often present at one of the subtidal spawning sites and low densities are detected acoustically (Morrison and Davoren 2024). We used a published target strength-length relationship for capelin (Rose 1998) to convert s_a into number of fish·m^-2 (i.e. density) and applied the average length and mass of capelin captured at the spawning sites in each year to convert density into capelin biomass (g·m^-2).

Seabird counts were merged with capelin biomass within each 100 m segment along each survey. Although all air-breathing marine predators were counted during surveys, we focused analyses on the numerically dominant seabirds (see Davoren 2007, 2013a, 2024), which included breeding seabirds (alcids, Northern Gannets, gulls), as well as non-breeding seabirds (shearwaters; Davoren 2007, Davoren 2013a). We further focused the analysis on birds that were more likely to be foraging by excluding counts of flying seabirds for species/groups that do not likely forage during flight (i.e., alcids, shearwaters; Davoren 2013a). During the surveys, alcid species included primarily Common Murres (96.6%), but also Razorbills (0.5%) and Atlantic Puffins (2.8%). Due to inter-annual variation in the number of each shearwater species (Carvalho and Davoren 2019), Sooty and Great shearwaters were grouped (Davoren 2013a). Counts of Northern Gannets and large gull species on the water and flying within the strip transect were included in the analysis as both groups forage during flight and birds on water may suggest recent foraging locations (Weseloh et al. 2020, Good 2020, Mowbray 2020). Gull species included American Herring Gulls (83.1%) and Great Black-backed Gulls (16.9%) and were grouped for analysis. The total number of birds per survey (i.e. sum of all 100 m segments) was calculated for each seabird species/group to quantify four seabird variables per survey: the total number of alcids (pursuit-divers), large gulls (surface-feeders), Northern Gannets (plunge-divers) and shearwaters (pursuit-divers).

As variation in prey characteristics other than biomass have been shown to influence predator responses (Maniscalco et al. 1998, Ostrand et al. 1998, Benoit-Bird et al. 2011, 2013, Sigler et al. 2017, Johnson and Davoren 2021b), we also quantified shoal characteristics for each survey. As this process was time-consuming, we only quantified shoal characteristics across the first seven years of surveys (2009-2010, 2012, 2014-2017). For each survey, fish shoals were identified by visually assessing each echogram in Echoview software, following Johnson and Davoren (2021b). The majority (97%) of shoals observed were identified as capelin based on capelin-likely s_v thresholds at different depths, along with the distinctive shape of capelin shoals (see Davoren et al. 2006), indicating that few other shoaling species were encountered in the survey area. Shoals were not included if prey were scattered in low density along the surface, which were often combined with bubble trails from birds (Benoit-Bird et al. 2011). Due to the dominance of capelin in the survey area, two characteristics of each capelin shoal were estimated following Davoren et al. (2006). First, we measured the maximum horizontal (width, m) and vertical (height, m) length of each shoal and then estimated the two-dimensional area of each shoal by multiplying the height by the width. Second, the acoustic biomass (area backscattering coefficient, or s_a, m²·m^-2; MacLennan et al. 2002) of the shoal was determined by integrating acoustic signals using a higher minimum s_v threshold of -70 dB to better define the prey shoal. Although depth is an important shoal characteristic for seabirds because it reflects prey accessibility for air-breathing predators (e.g., Ostrand et al. 1998, Womble et al. 2014), this was not included as a characteristic because data exploration revealed that >90% of shoals were accessible within 5-10 m from the ocean surface due to the shallow nature of the surveyed area (15-50 m). As capelin shoals could be predictably found in the survey area once capelin arrived inshore to spawn (Davoren et al. 2003a, 2006), shoal persistence on a fine scale (~1 km) was not measured, as in other studies (Sigler et al. 2017). Most shoals (>95%) matched previous descriptions of ephemeral shoals moving toward, away and between spawning sites (see Davoren et al. 2006) but a few each year matched previous descriptions of large, stationary spawning shoals (see Davoren et al. 2006) that could be predictably found on a fine scale (< 1 km) at discrete spawning sites across 1-2 weeks. Overall, the following four prey variables were quantified for each survey: mean fish biomass (g·m^-2) over the survey (i.e. average capelin biomass over all 100 m segments along the survey, ‘survey biomass’), mean capelin biomass (g·m^-2) within shoals (‘shoal biomass’), mean area of capelin shoals (m²; ‘shoal area’) and the number of shoals per survey. Survey biomass was calculated for surveys in all years of this study (2009-2010, 2012, 2014-2020), while shoal characteristics were only calculated for the first seven years (2009-2010, 2012, 2014-2017).