DOI: 10.5061/dryad.p8cz8w9q1
title: European shag provisioning foraging dives

description: Foraging dives in birds and mammals involve complex physiological and
 behavioural adaptations to cope with the breaks in normal respiration.
 Optimal dive strategies should maximise the proportion of time spent under
 water actively foraging versus the time spent on the surface. Oxygen
 loading and carbon-dioxide dumping carried out on the surface could
 involve recovery from the consequences of the last dive and/or preparation
 in anticipation of the next dive depth and duration. However, few studies
 have properly explored the causal pattern of effects within such dive
 cycles, which is crucial prior to any assessment of optimal dive
 strategies. Using Time Depth Recorders and Global Positioning System
 loggers, we recorded over 42,000 dives by 39 pairs of male and female
 European shags (Phalacrocorax aristotelis). Dives either involved a
 straight descent and ascent, presumably reflecting an unsuccessful search
 for prey, or a descent followed by horizontal movement followed by an
 ascent, presumably reflecting active hunting pursuit of pelagic prey.
 Males were larger than females, but we were unable to distinguish between
 sex effects and the non-linear effects of body mass on dive behaviour.
 Path analysis showed that within-individual dive-to-dive variation in
 surface times can best be explained as recovery from the previous dive. As
 expected in a pelagic hunter with unpredictable dive durations, there was
 no evidence of anticipatory preparation of oxygen stores in pre-dive
 surface durations. Among-individual variation in dives showed that body
 mass directly affected descent durations, but individual variation in all
 other dive and surface durations was driven by variation in descent
 duration, suggesting a critical role for dive depth in overcoming
 body-mass dependent effects of hydrodynamic/wave drag and buoyancy. Our
 analyses tests for the first time certain critical assumptions for studies
 assessing optimal dive strategies in birds and mammals, thereby revealing
 new details and avenues for research concerning adaptive diving behaviour.
description: Study
 site:<br> The Sklinna archipelago, situated about 20km off the coast
 of Vikna in Trøndelag, Central-Norway (65°12’N 10°59’E), holds one of the
 largest shag colonies in Norway with ca. 2,000 breeding pairs in
 2017. Animal
 welfare note:<br> Capture and handling of birds were approved by the
 Norwegian Environment Agency (2013/2306, 2014/2179, 2015/3042, 2016/3366,
 2017/4069, 2018/607) and the Norwegian Animal Research Authority
 (5148-2013/34672 (years 2013-2015), 7484-2015/55385 (years 2015-2017),
 12163-2017/67495 (years 2017-2019). All handling of birds were done by
 Felasa C approved persons, or under supervision of such
 persons.  Data
 Collection:<br> The fieldwork was conducted during June-July
 2013-2018, including 78 birds (39 pairs) over 6 different breeding
 seasons. Chick rearing shags were chosen based on their nest accessibility
 and how ‘protective’ the pairs were, as those that aggressively stayed
 around the nest were easier to capture/recapture. Parental birds were
 fitted with loggers when nestlings were approximately 5-35 days old.
 Nestling age was determined using morphological criteria determined from
 control nests (from nesting areas in similar habitat within the Sklinna
 colony) checked every fifth day. The shags were captured and then
 recaptured at their nest by hand or using snares. Each individual was
 fitted with a GPS-logger (i-gotU GT-120, Mobile Action Technology,
 re-fitted in heat-shrink tubes) and Time Depth Recorders (TDR, G5, CEFAS
 Technology). TDR-loggers were attached to the GPS logger prior to
 instrumentation, and the loggers were attached to 3-4 middle tail feathers
 using TESA® tape. The maximum logger deployment weight was 30.6g,
 corresponding to 1.6% and 1.8% of mean body mass of males and females,
 respectively. The GPS loggers recorded location (± 10m) every 30 sec, and
 the TDR recorded water depth below (± 0.1m) every second. The loggers were
 removed during recapture after approximately 2-5 days. Deployment of
 loggers normally required less than 3 min of handling and retrieval less
 than 10 min, and no disturbance effects were noted in either adults or
 their chicks. In cases where there were signs of parental disturbance in
 the form of decreased nestling provisioning, then the second parent was
 not captured, and so these pairs were not included in the study.
 The sex of adults was
 determined initially by body size features and ultimately via their
 vocalizations (Koffijberg and Van Eerden, 1995; Cramp and Simmons, 1977),
 because males and females made very distinct types of calls whilst
 defending the nest at our approach (Snow, 1960). At capture, body mass was
 obtained using a Pesola spring balance (accuracy ± 10g). Both adults in
 the pair were fitted with recording instruments during the same breeding
 season, although not overlapping in time, usually within only a few days
 of each other. At recapture, biometric measures were obtained (wing length
 (ruler ± 1mm), head and bill length (digital calliper ± 1mm) and body mass
 (see above)). Adult female average mass was 1610g (range 1370-1860g),
 whilst average adult male mass was 1920g (range 1660-2280g). Growth data
 (i.e. capture- recapture difference in chick weight) were collected for
 all nests during the time of recording and these measurements were
 compared to the control area within the same colony (see above) containing
 50 nests where adults were not fitted with loggers. There was rarely
 indication of parents reducing their provisioning rates or changing any
 patterns of nestling feeding while fitted with loggers. There were no
 obvious differences in the number of surviving chicks in experimental
 versus neighbouring control nests, aside from impeded survival due to gull
 predation. Data
 Handling:<br> Data handling and simulations was programmed in R
 3.5.1 (R Core Team, 2018) and the TDR raw data was analysed with the
 package DiveMove (Luque, 2007). The total number of dives in this study
 was 46,103. The surface for dives was calibrated at ±1m, so that no dive
 movement less than 1m depth was counted as a real dive, which helped to
 remove possible non-foraging ‘cleaning’ dives (Christensen-Dalsgaard
 <i>et al</i>., 2017). The time submerged during foraging dives
 was divided into vertical descents and ascents involving &lt;1m
 horizontal movement versus &gt;1m horizontal movements. Such
 horizontal movement was calibrated with the package DiveMove’s Zero-Offset
 Corrected (ZOC) method (Luque, 2007), smoothed using ±4m depth filters,
 and registered as dive bottom duration. Dives were classified into two
 types according to the presence/absence of this horizontal dive bottom
 duration: U-shaped (with a horizontal dive bottom) versus V-shaped (with
 no horizontal dive bottom) dives (see Supplementary Materials A1). Pre-
 and post-dive durations at the surface longer than 360s were used to
 separate dive bouts (i.e. distinct sequences of successive dives at one
 location) whenever surface durations were too long to be explained by
 simple replenishment of O<sub>2</sub> storages or momentary
 resting within a dive bout. GPS coordinates for each dive were assigned as
 the closest coordinates recorded within 30 secs before and/or after the
 dive (i.e. GPS locations could not be recorded during dives). GPS data
 were processed using R library ggmap (Kahle and Wickham, 2013). Merging,
 combining, and sorting of the data set was performed using the package
 dplyr (Wickham <i>et al.</i>, 2018), and plots were generated
 by ggplot2 (Wickham, 2016). A total 24 ‘locations’ were identified as
 distinct places where most dives occurred (i.e. clusters of dives
 surrounded by areas with no dives), and distinguished as areas of uniform
 average depth and foraging conditions as determined from a topographical
 base map by Kystverket (https://kart.kystverket.no/). GPS coordinates were thus abbreviated to 2 decimal labels based on these dive locations, whilst the geographical size and number of observations varied between locations.