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European shag provisioning foraging dives

Citation

Carlsen, Astrid; Wright, Jonathan; Lorentsen, Svein-Håkon (2021), European shag provisioning foraging dives, Dryad, Dataset, https://doi.org/10.5061/dryad.p8cz8w9q1

Abstract

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.

Methods

Study site:
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:
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:
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:
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 et al., 2017). The time submerged during foraging dives was divided into vertical descents and ascents involving <1m horizontal movement versus >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 O2 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 et al., 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.

Funding

Norwegian Oil and Gas Association

Norwegian Ministry of Petroleum and Energy via the Research Council of Norway, Award: 192141

Norwegian Ministry of Environment via its Environmental Agency

Norwegian Oil and Gas Association

Norwegian Ministry of Petroleum and Energy via the Research Council of Norway, Award: 192141

Norwegian Ministry of Environment via its Environmental Agency