DOI: 10.5061/dryad.jq2bvq8k8
title: Aggregative responses of marine predators to a pulsed resource

description: 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.
description: 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<sup>-1</sup> and pulse duration of 0.4 ms. Raw high
 resolution hydroacoustic data (s<sub>v</sub>, 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<sub>a</sub>,
 m<sup>2</sup>·m<sup>-2</sup>; MacLennan et al.
 2002), we integrated acoustic signals using a minimum
 s<sub>v</sub> 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 (<em>Ammodytes</em>
 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<sub>a</sub> into number of
 fish·m<sup>-2</sup> (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<sup>-2</sup>). 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<sub>v</sub> 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<sub>a</sub>,
 m<sup>2</sup>·m<sup>-2</sup>; MacLennan et al.
 2002) of the shoal was determined by integrating acoustic signals using a
 higher minimum s<sub>v</sub> 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
 &gt;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 (&gt;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 (&lt; 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<sup>-2</sup>) over the survey (i.e. average capelin
 biomass over all 100 m segments along the survey, ‘survey biomass’), mean
 capelin biomass (g·m<sup>-2</sup>) within shoals (‘shoal
 biomass’), mean area of capelin shoals (m<sup>2</sup>; ‘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).
description: # Aggregative responses of marine predators to a pulsed resource
 [https://doi.org/10.5061/dryad.jq2bvq8k8](https://doi.org/10.5061/dryad.jq2bvq8k8) ## Description of the data and file structure | Column | Description | | :----------------------- | :---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | | Date | Date of each survey in day-month-year format | | Ordinal Date | Ordinal date of each survey  | | DateSpawn | Ordinal date of first day of capelin spawning in the study area in each year | | DiffSpawn | Number of days between the survey date and the first date of capelin spawning (negative numbers indicate the survey date was before spawning, positive numbers indicate the survey date was after spawning) | | no.bins | Number of 100 m horizonal bins covered during each survey | | Ave.survey.g.m2 | Average fish biomass across all 100 m bins in each survey (g/m2) | | No.Schools | Number of schools across all 100 m bins in each survey (these data were not collected after 2017) | | AveShoalBiomass | Average fish biomass of all shoals across all 100 m bins in each survey (g/m2) (these data were not collected after 2017) | | Ave.Area.Shoal..h.w\..m2 | Average area of each fish shoal (maximum horizontal length multiplied by vertical height) across all 100 m bins in each survey (m2) (these data were not collected after 2017) | | TOTAL.BIRDS | Total number of all seabirds across all 100 m bins in each survey | | TOTAL.ALCID | Total number of alcids (Common Murres, Razorbills, Atlantic Puffins) on the water across all 100 m bins in each survey | | TOTAL LARUS | Total number of Larus gulls (Herring Gulls, Great Black-backed gulls)  on the water and flying across all 100 m bins in each survey | | NOGA | Total number of Northern Gannets on the water and flying across all 100 m bins in each survey | | GRSH\&SOSH | Total number of Great and Sooty Shearwaters on the water across all 100 m bins in each survey |